Rewrite as a probabilistic model with walk-forward evaluation.
Replace the 2024 model (model.py, ~2000 lines) with the btcmodel package, the baseline for future work: - Forecasts are quantiles of log price at each horizon, scored with CRPS in a walk-forward backtest (origins every 30 days from 2014, horizons 1 month to 4 years). Skill is relative to a zero-drift random walk, with circular block-bootstrap intervals and a count of independent windows. - Development data stops at 2024-11-26, the last day the 2024 model saw. Later outcomes are a holdout, scored only by `backtest --holdout`. - Models: random_walk, drift_rw, and cycle (the 2024 model's cycle-position drift, now kernel-smoothed and recency-weighted). On development data nothing beats the random walk with confidence; cycle loses at every horizon. - Prices: the Investing.com archive moves to data/ (cut at 2024-11-26; its last row was intraday) and is extended with Coinbase daily closes by `update`. Also: Nix flake dev shell (Python 3.13, pandas 3), ruff in place of black, pytest suite, and a rewritten README. NOTES.md is removed as inaccurate, and poetry is dropped.
This commit is contained in:
+6
-2
@@ -1,4 +1,8 @@
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bitcoin_*.png
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bitcoin_*.txt
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/output
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__pycache__/
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.pytest_cache/
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.ruff_cache/
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.venv/
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result
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scratch.py
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.DS_Store
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@@ -1,361 +0,0 @@
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# Bitcoin Price Model Documentation
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## Model Overview
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A probabilistic price projection model combining log returns analysis, cycle awareness, and Monte Carlo simulation. The model generates projected price ranges with confidence intervals, balancing short-term market dynamics with long-term cyclical patterns.
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## Core Design Principles
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### 1. Return Analysis
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- Uses log returns for better handling of exponential growth
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- Combines multiple timeframes for volatility estimation
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- Implements adaptive window sizing based on market conditions
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- Handles volatility clustering through regime-aware adjustments
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|
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### 2. Cycle Integration
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- Recognizes Bitcoin's ~4 year (1460 day) halving cycle
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- Maps historical returns to cycle positions (0-1 scale)
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- Adjusts expectations based on position in cycle
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- Handles transitions between cycles with uncertainty scaling
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|
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### 3. Market Era Recognition
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Three distinct eras with specific characteristics:
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- Early (2013-2017): Higher base volatility, conservative trends
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- Transition (2017-2020): Futures market introduction period
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- Mature (2020+): Institutional participation, reduced base volatility
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|
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### 4. Uncertainty Estimation
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- Generates both point estimates and confidence intervals
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- Adapts uncertainty based on market conditions
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- Uses asymmetric volatility response
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- Implements dynamic confidence interval calibration
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|
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## Architecture
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### Key Components
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1. **Trend Analysis (`analyze_trends`)**
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- Calculates cycle-position-specific returns
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- Applies position-aware smoothing
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- Handles cycle boundaries
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|
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2. **Volatility Estimation (`calculate_volatility`)**
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- Adaptive window sizing
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- Multi-timeframe integration
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- Era-specific scaling
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- Regime detection and response
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|
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3. **Price Projection (`project_prices`)**
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- Monte Carlo simulation engine
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- Dynamic uncertainty scaling
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- Confidence interval calculation
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- Trend integration
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|
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4. **Projection Adjustment (`get_projection_adjustments`)**
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- Time-varying uncertainty scaling
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- Market condition response
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- Cycle position awareness
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- Minimum uncertainty bounds
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# Model Performance & Validation
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## Performance Characteristics
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|
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### Normal Market Conditions
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- MAPE: 30-40% typical
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- 95% CI Coverage: ~95%
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- 68% CI Coverage: ~73%
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- Best performance in mature market periods (2020+)
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- Most reliable for 3-6 month horizons
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|
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### Stress Periods
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- MAPE: 30-60%
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- 95% CI Coverage: ~95%
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- 68% CI Coverage: ~76%
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- Wider but well-calibrated confidence intervals
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- Maintains reliability through increased uncertainty
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|
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### Key Strengths
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1. Consistent confidence interval coverage
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2. Rapid adaptation to volatility changes
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3. Robust handling of cycle transitions
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4. Well-calibrated uncertainty estimates
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|
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### Known Limitations
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1. Higher error during market structure changes
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2. Increased uncertainty in early cycle periods
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3. Limited incorporation of external factors
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4. May underestimate extreme events
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|
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## Validation Framework
|
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|
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### Backtest Configuration
|
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- Minimum training period: 8 years
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- Validation period: 2 years
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- Rolling window approach
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- Separate evaluation of normal/stress periods
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|
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### Key Test Periods
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1. **Cycle Transitions**
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- Pre/post halving periods
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- Historical halvings (2016, 2020, 2024)
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- Cycle peak/trough transitions
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|
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2. **Market Structure Changes**
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- Futures introduction (2017)
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- Institution adoption (2020-2021)
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- Major market events (e.g., COVID crash)
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|
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3. **Recent History**
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- 2021 bull market
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- 2022 drawdown
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- 2024 recovery
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|
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### Validation Metrics
|
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1. **Accuracy Measures**
|
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- MAPE (Mean Absolute Percentage Error)
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- RMSE (Root Mean Square Error)
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- Maximum deviation
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|
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2. **Calibration Measures**
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- Confidence interval coverage
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- Uncertainty estimation accuracy
|
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- Regime transition handling
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|
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3. **Stability Measures**
|
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- Parameter sensitivity
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- Training period dependence
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- Regime change response
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|
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# Technical Implementation
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|
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## Core Functions
|
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|
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### Volatility Calculation
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```python
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def calculate_volatility(df, short_window=30, medium_window=90, long_window=180):
|
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"""
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Adaptive volatility calculation combining multiple timeframes.
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|
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Features:
|
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- Dynamic window sizing based on market conditions
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- Era-specific scaling factors
|
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- Regime-aware adjustments
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- Robust error handling and fallbacks
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"""
|
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```
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|
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Key parameters:
|
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- `short_window`: Fast response (default 30 days)
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- `medium_window`: Primary estimate (default 90 days)
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- `long_window`: Stability baseline (default 180 days)
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|
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Adaptive features:
|
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- Windows shrink in high volatility periods
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- Expand during low volatility
|
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- Minimum size constraints for stability
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- Weighted combination based on regime
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|
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### Cycle Position
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```python
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def get_cycle_position(date, halving_dates):
|
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"""
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Calculate position in halving cycle (0 to 1).
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0 = halving event
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1 = just before next halving
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"""
|
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```
|
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|
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Position calculation:
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- Linear interpolation between halvings
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- Special handling for pre-first-halving
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- Extension mechanism for future cycles
|
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- Built-in boundary condition handling
|
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|
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### Price Projection
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```python
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def project_prices(df, days_forward=365, simulations=1000,
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confidence_levels=[0.95, 0.68]):
|
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"""
|
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Generate price projections with confidence intervals.
|
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|
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Core simulation parameters:
|
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- Number of paths: 1000
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- Confidence levels: 95% and 68%
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- Dynamic uncertainty scaling
|
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"""
|
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```
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|
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## Data Requirements
|
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|
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### Input Data
|
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Minimum fields:
|
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- Date
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- Close price
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- Trading volume (optional)
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- High/Low (optional)
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|
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Format requirements:
|
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- Daily data preferred
|
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- Sorted chronologically
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- No missing dates
|
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- Prices > 0
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|
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### Training Data
|
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Minimum requirements:
|
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- 2 years for basic operation
|
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- 8 years recommended
|
||||
- Must include at least one cycle transition
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- Should span multiple market regimes
|
||||
|
||||
## Error Handling
|
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|
||||
### Data Validation
|
||||
- Missing value detection and interpolation
|
||||
- Outlier identification
|
||||
- Zero/negative price handling
|
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- Volume anomaly detection
|
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|
||||
### Runtime Guards
|
||||
- Minimum data length checks
|
||||
- Window size validation
|
||||
- Numerical stability checks
|
||||
- Regime transition handling
|
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|
||||
### Fallback Mechanisms
|
||||
1. Simple volatility calculation
|
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2. Default uncertainty estimates
|
||||
3. Conservative parameter sets
|
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4. Standard cycle assumption
|
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|
||||
## Memory and Performance
|
||||
|
||||
### Optimization Features
|
||||
- Efficient numpy operations
|
||||
- Vectorized calculations where possible
|
||||
- Smart data windowing
|
||||
- Caching of intermediate results
|
||||
|
||||
### Resource Usage
|
||||
Typical requirements for 10-year dataset:
|
||||
- Memory: ~100MB
|
||||
- CPU: ~2-5 seconds per projection
|
||||
- Storage: Negligible
|
||||
|
||||
### Parallelization
|
||||
- Multiprocessing support for backtests
|
||||
- Independent path simulation
|
||||
- Multiple period analysis
|
||||
- Backtest parallelization
|
||||
|
||||
# Development History & Evolution
|
||||
|
||||
## Major Versions
|
||||
|
||||
### Version 1.0 (Initial Implementation)
|
||||
- Basic log return analysis
|
||||
- Fixed volatility windows
|
||||
- Simple cycle position calculation
|
||||
- Base Monte Carlo simulation
|
||||
|
||||
### Version 2.0 (Market Structure)
|
||||
- Added era-based adjustments
|
||||
- Improved cycle handling
|
||||
- Multiple timeframe volatility
|
||||
- Enhanced Monte Carlo engine
|
||||
|
||||
### Version 3.0 (Current)
|
||||
- Adaptive volatility windows
|
||||
- Dynamic uncertainty scaling
|
||||
- Improved regime detection
|
||||
- Enhanced confidence interval calibration
|
||||
|
||||
## Key Improvements
|
||||
|
||||
### Volatility Estimation
|
||||
1. **Fixed → Adaptive Windows**
|
||||
- Initial: Fixed 30/90/180 day windows
|
||||
- Current: Dynamic sizing based on regime
|
||||
- Result: Better regime transition handling
|
||||
|
||||
2. **Uncertainty Calibration**
|
||||
- Initial: Fixed scaling factors
|
||||
- Current: Market-aware dynamic scaling
|
||||
- Result: More reliable confidence intervals
|
||||
|
||||
3. **Era Recognition**
|
||||
- Initial: Single model for all periods
|
||||
- Current: Era-specific adjustments
|
||||
- Result: Better handling of market evolution
|
||||
|
||||
### Simulation Engine
|
||||
1. **Path Generation**
|
||||
- Initial: Basic random walks
|
||||
- Current: Regime-aware path simulation
|
||||
- Result: More realistic price trajectories
|
||||
|
||||
2. **Confidence Intervals**
|
||||
- Initial: Fixed width
|
||||
- Current: Dynamic, asymmetric intervals
|
||||
- Result: Better calibrated uncertainty
|
||||
|
||||
## Failed Experiments
|
||||
|
||||
### 1. Complex Regime Detection
|
||||
- Attempted multiple indicator fusion
|
||||
- Added excessive complexity
|
||||
- Reduced model stability
|
||||
- Reverted to simpler approach
|
||||
|
||||
### 2. Machine Learning Integration
|
||||
- Tested neural network components
|
||||
- Reduced interpretability
|
||||
- Inconsistent improvements
|
||||
- Kept traditional statistical approach
|
||||
|
||||
### 3. External Factor Integration
|
||||
- Tried incorporating macro indicators
|
||||
- Added noise to projections
|
||||
- Complicated parameter estimation
|
||||
- Maintained focus on price dynamics
|
||||
|
||||
## Recent Improvements (2024)
|
||||
|
||||
### Adaptive Volatility Windows
|
||||
- Implementation: Dynamic window sizing
|
||||
- Purpose: Better regime handling
|
||||
- Results:
|
||||
- Improved 95% CI coverage to ~95%
|
||||
- Better stress period handling
|
||||
- More reliable uncertainty estimates
|
||||
|
||||
### Performance Metrics
|
||||
Normal Periods:
|
||||
- MAPE: 39.9%
|
||||
- RMSE: $12,007
|
||||
- 95% CI Coverage: 95.9%
|
||||
- 68% CI Coverage: 72.5%
|
||||
|
||||
Stress Periods:
|
||||
- MAPE: 32.8%
|
||||
- RMSE: $12,794
|
||||
- 95% CI Coverage: 95.2%
|
||||
- 68% CI Coverage: 76.1%
|
||||
|
||||
## Future Directions
|
||||
|
||||
### Short Term
|
||||
1. Fine-tune adaptive parameters
|
||||
2. Improve transition period handling
|
||||
3. Enhanced backtest framework
|
||||
4. Additional regime indicators
|
||||
|
||||
### Medium Term
|
||||
1. Cycle strength indicators
|
||||
2. Volume analysis integration
|
||||
3. Improved documentation
|
||||
4. Performance optimization
|
||||
|
||||
### Long Term
|
||||
1. Real-time adaptation framework
|
||||
2. Advanced regime detection
|
||||
3. Market microstructure integration
|
||||
4. External API integration
|
||||
@@ -6,18 +6,108 @@
|
||||
|
||||
**Don't take this seriously. It's all in good fun.**
|
||||
|
||||
I decided to have fun and ask Anthropic's Claude AI (3.6 Sonnet) to help build
|
||||
a Bitcoin price model, using data from [Investing.com](https://www.investing.com/crypto/bitcoin/historical-data).
|
||||
## The 2024 edition
|
||||
|
||||
The model is still a mess: lots of redundant code as we went through various
|
||||
methods for projecting future prices; the plots' colors don't render correctly.
|
||||
In November 2024 I asked Claude (3.6 Sonnet, via copy and paste in Claude Web)
|
||||
to help build a Bitcoin price model. After a lot of branching it produced ~2000
|
||||
lines of cycle analysis, "market fundamentals", era adjustments and Monte Carlo,
|
||||
and forecasts like this one (as of 2024-11-14):
|
||||
|
||||
I feel like I'm dangerous enough to know what to ask for out of a model, but
|
||||
not knowledgeable enough evaluate whether what Claude produced actually makes
|
||||
any sense. (Stats class in college was a long time ago...)
|
||||
|
||||
|
||||
## tl;dr show me the projection!
|
||||
In September 2026 a newer Claude scored the forecasts it made on 2024-11-27
|
||||
against what actually happened:
|
||||
|
||||
As of writing (2024-11-14), here is what the model generates:
|
||||
| | 2024 forecast (median) | Actual |
|
||||
|---|---|---|
|
||||
| Cycle top | 2025-10-21, $199K | 2025-10-06, $124.7K |
|
||||
| 2026-06-30 | $133K (95% floor $65K) | $58.5K |
|
||||
| 2026-09-23 | $124K | $84.4K |
|
||||
|
||||
|
||||
It called the timing of the top within 15 days, 19 months ahead, but put the
|
||||
level ~60% too high. "The price stays at $92.7K" was a better forecast (18%
|
||||
mean error against 68%). Its "95%" intervals were really ~81% intervals past a
|
||||
year out, thanks to a fudge factor that narrowed them at long horizons.
|
||||
|
||||
That code lives in the jj/git history. This is the rewrite.
|
||||
|
||||
## How it works now
|
||||
|
||||
A forecast is a probability distribution of the price at each horizon, not a
|
||||
line. Every model produces quantiles of log price, and every model is scored
|
||||
the same way:
|
||||
|
||||
- **Walk-forward.** Every 30 days from 2014 on, each model sees only the data up
|
||||
to that day and forecasts 1 month to 4 years ahead.
|
||||
- **CRPS.** Each forecast is scored against what happened with the continuous
|
||||
ranked probability score, in log-price units (0.1 ≈ "typically 10% off"). It
|
||||
rewards being sharp and being calibrated at once, and can't be gamed by
|
||||
narrowing or widening intervals.
|
||||
- **Baselines.** Skill is reported relative to a zero-drift random walk, with a
|
||||
90% block-bootstrap interval. Nearby forecasts overlap heavily, so the report
|
||||
also shows `windows`: the number of genuinely independent outcomes.
|
||||
- **Holdout.** Development only sees data up to 2024-11-26, the last day the
|
||||
2024 model saw. Everything after it is held out, and is scored only by
|
||||
`just holdout`, once per round of model changes. (Caveat: we already know
|
||||
roughly what happened in 2025-26, so it isn't perfectly blind.)
|
||||
|
||||
### Models
|
||||
|
||||
- `random_walk`: zero drift; "it stays about here, give or take".
|
||||
- `drift_rw`: drift equal to the last four years' average; "it keeps doing what
|
||||
it did last cycle".
|
||||
- `cycle`: the 2024 model's one real idea. Expected return depends on the day of
|
||||
the halving cycle, estimated from past cycles, with recent cycles weighted
|
||||
more.
|
||||
|
||||
### Findings so far (development data)
|
||||
|
||||
- Nothing beats the random walk with any confidence at any horizon. At 2-4
|
||||
years there are only 3-5 independent outcomes in the whole history.
|
||||
- `drift_rw` leads at 2-4 years (+13-18% skill, but the intervals span zero).
|
||||
- `cycle` loses to both at every horizon, and so does every setting tried
|
||||
(recency half-life 0.25-2 cycles, smoothing bandwidth 15-60 days). The cycle
|
||||
*shape* costs accuracy. The level is the problem: each cycle has grown less
|
||||
than the last (log return 4.0, 2.6, 2.0, i.e. roughly ×55, ×13, ×7), so any
|
||||
average of past cycles overshoots.
|
||||
|
||||
That points at diminishing returns as the structure worth modelling, e.g. a
|
||||
power-law trend, which is next.
|
||||
|
||||
## Usage
|
||||
|
||||
With Nix:
|
||||
|
||||
```sh
|
||||
nix develop
|
||||
just update # fetch new daily prices from Coinbase
|
||||
just backtest # score models on development data -> output/backtest/
|
||||
just forecast # forecast from the latest price -> output/forecast/
|
||||
just test
|
||||
just holdout # score on held-out outcomes; sparingly
|
||||
```
|
||||
|
||||
Without Nix, any Python 3.13 with numpy, pandas 3, scipy and matplotlib works:
|
||||
`python -m btcmodel --help`.
|
||||
|
||||
## Layout
|
||||
|
||||
```
|
||||
btcmodel/
|
||||
data.py price loading (Investing.com archive + Coinbase), dev cutoff
|
||||
halving.py halving calendar, position in cycle
|
||||
forecast.py Forecast (quantiles of log price), CRPS, PIT
|
||||
evaluate.py walk-forward backtest and summary
|
||||
models/ one file per model family; register new ones in __init__.py
|
||||
plots.py fan chart, skill and calibration charts
|
||||
```
|
||||
|
||||
A model is any object with a `name` and
|
||||
`forecast(history, horizons) -> Forecast`. `history` is a date-indexed frame
|
||||
holding everything up to the forecast origin and nothing after it. Other data
|
||||
sources (hash rate, on-chain metrics, macro series) can join it as extra
|
||||
columns. Anything published with a lag must be shifted to the date it was
|
||||
actually available, or the backtest will quietly cheat.
|
||||
|
||||
Price data: [Investing.com](https://www.investing.com/crypto/bitcoin/historical-data)
|
||||
through 2024-11-26, then Coinbase Exchange daily closes (UTC).
|
||||
|
||||
@@ -0,0 +1,100 @@
|
||||
"""
|
||||
Command line entry point.
|
||||
|
||||
python -m btcmodel update fetch new daily prices from Coinbase
|
||||
python -m btcmodel backtest score models on development data
|
||||
python -m btcmodel backtest --holdout score models on outcomes after DEV_CUTOFF
|
||||
python -m btcmodel forecast forecast from the latest price
|
||||
"""
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from . import data, evaluate, plots
|
||||
from .models import MODELS
|
||||
|
||||
FORECAST_REPORT_HORIZONS = (182, 365, 730, 1095, 1460)
|
||||
FORECAST_REPORT_LEVELS = (0.05, 0.25, 0.5, 0.75, 0.95)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(prog="btcmodel", description="Bitcoin price model")
|
||||
parser.add_argument("-o", "--output", type=Path, default=Path("output"))
|
||||
parser.add_argument("-m", "--models", nargs="+", choices=list(MODELS), default=list(MODELS))
|
||||
commands = parser.add_subparsers(dest="command", required=True)
|
||||
commands.add_parser("update", help="fetch new daily prices")
|
||||
backtest = commands.add_parser("backtest", help="walk-forward evaluation")
|
||||
backtest.add_argument(
|
||||
"--holdout",
|
||||
action="store_true",
|
||||
help=f"score outcomes after {data.DEV_CUTOFF:%Y-%m-%d} (don't use while developing)",
|
||||
)
|
||||
commands.add_parser("forecast", help="forecast from the latest price")
|
||||
args = parser.parse_args()
|
||||
models = [MODELS[name] for name in args.models]
|
||||
|
||||
if args.command == "update":
|
||||
added = data.update_coinbase()
|
||||
print(f"added {added} days; latest {data.load_prices().index[-1]:%Y-%m-%d}")
|
||||
elif args.command == "backtest":
|
||||
run_backtest(models, args.output, args.holdout)
|
||||
elif args.command == "forecast":
|
||||
run_forecast(models, args.output)
|
||||
|
||||
|
||||
def run_backtest(models, output: Path, holdout: bool) -> None:
|
||||
if holdout:
|
||||
name, prices = "holdout", data.load_prices()
|
||||
scores = evaluate.backtest(models, prices, score_after=data.DEV_CUTOFF)
|
||||
else:
|
||||
name, prices = "backtest", data.load_prices(until=data.DEV_CUTOFF)
|
||||
scores = evaluate.backtest(models, prices)
|
||||
out = output / name
|
||||
out.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
summary = evaluate.summarize(scores)
|
||||
report = (
|
||||
f"{name}: origins every {evaluate.ORIGIN_STEP_DAYS} days from "
|
||||
f"{evaluate.FIRST_ORIGIN:%Y-%m-%d}, outcomes through {prices.index[-1]:%Y-%m-%d}\n\n"
|
||||
+ evaluate.format_summary(summary)
|
||||
)
|
||||
print(report)
|
||||
(out / "report.txt").write_text(report + "\n")
|
||||
scores.to_csv(out / "scores.csv", index=False)
|
||||
summary.to_csv(out / "summary.csv", index=False)
|
||||
plots.skill_chart(summary, out / "skill.png", f"{name}: skill by horizon")
|
||||
plots.calibration_chart(summary, out / "calibration.png", f"{name}: interval coverage")
|
||||
print(f"\nwrote {out}/")
|
||||
|
||||
|
||||
def run_forecast(models, output: Path) -> None:
|
||||
prices = data.load_prices()
|
||||
horizons = np.arange(1, max(FORECAST_REPORT_HORIZONS) + 1)
|
||||
forecasts = {m.name: m.forecast(prices, horizons) for m in models}
|
||||
out = output / "forecast"
|
||||
out.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
rows = []
|
||||
for name, f in forecasts.items():
|
||||
for h in FORECAST_REPORT_HORIZONS:
|
||||
row = {"model": name, "date": f.dates[h - 1].date(), "horizon": h}
|
||||
for level in FORECAST_REPORT_LEVELS:
|
||||
row[f"p{level * 100:02.0f}"] = np.exp(f.quantile(level)[h - 1])
|
||||
rows.append(row)
|
||||
table = pd.DataFrame(rows)
|
||||
table.to_csv(out / "forecast.csv", index=False)
|
||||
|
||||
shown = table.copy()
|
||||
for column in shown.columns[3:]:
|
||||
shown[column] = shown[column].map(plots.price_formatter)
|
||||
print(f"from {prices.index[-1]:%Y-%m-%d} at {plots.price_formatter(prices.close.iloc[-1])}\n")
|
||||
print(shown.to_string(index=False))
|
||||
plots.fan_chart(prices, forecasts, out / "fan.png")
|
||||
print(f"\nwrote {out}/")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,117 @@
|
||||
"""Daily BTC-USD closing prices.
|
||||
|
||||
Two sources, stitched at ARCHIVE_END:
|
||||
|
||||
- data/investing.csv: the original Investing.com download (2010-07-18 onward).
|
||||
Its last row (2024-11-27) was an intraday snapshot, so it is cut a day early.
|
||||
- data/coinbase.csv: Coinbase Exchange daily candles (UTC days), appended by
|
||||
`python -m btcmodel update`.
|
||||
|
||||
Everything up to ARCHIVE_END is development data. Everything after it is the
|
||||
holdout: outcomes nobody had seen while the 2024 model was being built, and which
|
||||
model development here must not look at (see evaluate.py).
|
||||
"""
|
||||
|
||||
import datetime as dt
|
||||
import json
|
||||
import urllib.request
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
DATA_DIR = Path(__file__).resolve().parent.parent / "data"
|
||||
ARCHIVE_CSV = DATA_DIR / "investing.csv"
|
||||
COINBASE_CSV = DATA_DIR / "coinbase.csv"
|
||||
|
||||
ARCHIVE_END = pd.Timestamp("2024-11-26")
|
||||
DEV_CUTOFF = ARCHIVE_END
|
||||
|
||||
# 2010 has four distinct prices and no change on 89% of days; it is noise.
|
||||
DATA_START = pd.Timestamp("2011-01-01")
|
||||
|
||||
COINBASE_URL = "https://api.exchange.coinbase.com/products/BTC-USD/candles"
|
||||
COINBASE_MAX_CANDLES = 300
|
||||
|
||||
|
||||
def load_prices(
|
||||
until: pd.Timestamp | str | None = None, start: pd.Timestamp | str = DATA_START
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Load daily closes as a frame indexed by date, with a single `close` column.
|
||||
|
||||
Models receive a prefix of this frame, so additional data sources can be
|
||||
joined in as extra columns later without changing the model interface.
|
||||
"""
|
||||
archive = _read_archive()
|
||||
parts = [archive[archive.index <= ARCHIVE_END]]
|
||||
if COINBASE_CSV.exists():
|
||||
coinbase = pd.read_csv(COINBASE_CSV, index_col="date", parse_dates=["date"])
|
||||
parts.append(coinbase.loc[coinbase.index > ARCHIVE_END, ["close"]])
|
||||
df = pd.concat(parts).sort_index()
|
||||
|
||||
df = df[df.index >= pd.Timestamp(start)]
|
||||
if until is not None:
|
||||
df = df[df.index <= pd.Timestamp(until)]
|
||||
|
||||
expected = pd.date_range(df.index[0], df.index[-1], freq="D")
|
||||
missing = expected.difference(df.index)
|
||||
if len(missing):
|
||||
raise ValueError(f"{len(missing)} missing days, first {missing[0].date()}")
|
||||
if not (df["close"] > 0).all():
|
||||
raise ValueError("non-positive closing price")
|
||||
return df
|
||||
|
||||
|
||||
def log_returns(df: pd.DataFrame) -> pd.Series:
|
||||
"""Daily log returns of the close, without the leading NaN."""
|
||||
return np.log(df["close"]).diff().iloc[1:]
|
||||
|
||||
|
||||
def _read_archive() -> pd.DataFrame:
|
||||
raw = pd.read_csv(ARCHIVE_CSV, encoding="utf-8-sig", thousands=",")
|
||||
dates = pd.to_datetime(raw["Date"], format="%m/%d/%Y")
|
||||
return pd.DataFrame({"close": raw["Price"].astype(float).values}, index=dates.rename("date"))
|
||||
|
||||
|
||||
def update_coinbase() -> int:
|
||||
"""Append completed daily candles since the last stored day. Returns rows added."""
|
||||
if COINBASE_CSV.exists():
|
||||
existing = pd.read_csv(COINBASE_CSV, index_col="date", parse_dates=["date"])
|
||||
first = existing.index.max() + pd.Timedelta(days=1)
|
||||
else:
|
||||
existing = None
|
||||
first = ARCHIVE_END + pd.Timedelta(days=1)
|
||||
# Today's candle is still forming; only take finished UTC days.
|
||||
last = pd.Timestamp(dt.datetime.now(dt.UTC).date()) - pd.Timedelta(days=1)
|
||||
|
||||
rows = []
|
||||
chunk_start = first
|
||||
while chunk_start <= last:
|
||||
chunk_end = min(chunk_start + pd.Timedelta(days=COINBASE_MAX_CANDLES - 1), last)
|
||||
rows.extend(_fetch_candles(chunk_start, chunk_end))
|
||||
chunk_start = chunk_end + pd.Timedelta(days=1)
|
||||
if not rows:
|
||||
return 0
|
||||
|
||||
new = pd.DataFrame(rows, columns=["date", "close"]).set_index("date").sort_index()
|
||||
new = new[(new.index >= first) & (new.index <= last)]
|
||||
combined = new if existing is None else pd.concat([existing, new])
|
||||
combined = combined[~combined.index.duplicated(keep="last")].sort_index()
|
||||
combined.to_csv(COINBASE_CSV, date_format="%Y-%m-%d")
|
||||
return len(new)
|
||||
|
||||
|
||||
def _fetch_candles(start: pd.Timestamp, end: pd.Timestamp) -> list[tuple[pd.Timestamp, float]]:
|
||||
url = (
|
||||
f"{COINBASE_URL}?granularity=86400"
|
||||
f"&start={start:%Y-%m-%d}T00:00:00Z&end={end:%Y-%m-%d}T00:00:00Z"
|
||||
)
|
||||
# Coinbase rejects requests without a User-Agent.
|
||||
request = urllib.request.Request(url, headers={"User-Agent": "btcmodel"})
|
||||
with urllib.request.urlopen(request, timeout=30) as response:
|
||||
candles = json.load(response)
|
||||
# Each candle is [time, low, high, open, close, volume].
|
||||
return [
|
||||
(pd.Timestamp(dt.datetime.fromtimestamp(c[0], dt.UTC).date()), float(c[4])) for c in candles
|
||||
]
|
||||
@@ -0,0 +1,123 @@
|
||||
"""
|
||||
Walk-forward evaluation.
|
||||
|
||||
From each origin (every ORIGIN_STEP_DAYS from FIRST_ORIGIN), each model sees the
|
||||
data up to that day only and forecasts every horizon. Each forecast whose target
|
||||
date has been observed is scored against what happened.
|
||||
|
||||
Development runs load data only up to DEV_CUTOFF, so outcomes after it cannot
|
||||
influence model design. The holdout run scores only targets after DEV_CUTOFF.
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from .forecast import crps, pit
|
||||
from .models import BASELINE
|
||||
|
||||
HORIZONS = np.array([30, 91, 182, 365, 730, 1095, 1460])
|
||||
FIRST_ORIGIN = pd.Timestamp("2014-01-01")
|
||||
ORIGIN_STEP_DAYS = 30
|
||||
COVERAGES = (0.5, 0.8, 0.95)
|
||||
|
||||
|
||||
def backtest(
|
||||
models, data: pd.DataFrame, horizons=HORIZONS, score_after: pd.Timestamp | None = None
|
||||
) -> pd.DataFrame:
|
||||
"""One row per (model, origin, horizon) with an observed outcome."""
|
||||
log_close = np.log(data["close"])
|
||||
last = data.index[-1]
|
||||
rows = []
|
||||
for origin in pd.date_range(FIRST_ORIGIN, last, freq=f"{ORIGIN_STEP_DAYS}D"):
|
||||
targets = origin + pd.to_timedelta(horizons, unit="D")
|
||||
scored = targets <= last
|
||||
if score_after is not None:
|
||||
scored &= targets > score_after
|
||||
if not scored.any():
|
||||
continue
|
||||
history = data.loc[:origin]
|
||||
outcome = log_close.loc[targets[scored]].to_numpy()
|
||||
for model in models:
|
||||
f = model.forecast(history, horizons[scored])
|
||||
row = {
|
||||
"model": model.name,
|
||||
"origin": origin,
|
||||
"horizon": f.horizons,
|
||||
"outcome": outcome,
|
||||
"median": f.quantile(0.5),
|
||||
"crps": crps(f.log_quantiles, outcome),
|
||||
"pit": pit(f.log_quantiles, outcome),
|
||||
}
|
||||
for c in COVERAGES:
|
||||
lo, hi = f.interval(c)
|
||||
row[f"in{c:.0%}"] = (lo <= outcome) & (outcome <= hi)
|
||||
rows.append(pd.DataFrame(row))
|
||||
return pd.concat(rows, ignore_index=True)
|
||||
|
||||
|
||||
def summarize(scores: pd.DataFrame, n_boot: int = 2000, seed: int = 0) -> pd.DataFrame:
|
||||
"""
|
||||
Per model and horizon: mean CRPS, skill relative to the baseline, and coverage.
|
||||
|
||||
Skill is 1 - CRPS / baseline CRPS (positive = better than the baseline),
|
||||
with a 90% moving-block bootstrap interval over origins. Forecasts from
|
||||
nearby origins overlap heavily, so `windows` (the span covered divided by
|
||||
the horizon) is the honest count of independent outcomes. Treat intervals
|
||||
with fewer than ~5 windows as optimistic.
|
||||
"""
|
||||
rng = np.random.default_rng(seed)
|
||||
rows = []
|
||||
for horizon, at_h in scores.groupby("horizon"):
|
||||
base = at_h[at_h.model == BASELINE].set_index("origin")["crps"].sort_index()
|
||||
span = (base.index[-1] - base.index[0]).days + horizon
|
||||
block = max(1, min(int(np.ceil(horizon / ORIGIN_STEP_DAYS)), len(base) // 2))
|
||||
boot_index = _block_bootstrap_indices(len(base), block, n_boot, rng)
|
||||
for model, g in at_h.groupby("model", sort=False):
|
||||
m = g.set_index("origin")["crps"].reindex(base.index).to_numpy()
|
||||
boot = 1 - m[boot_index].mean(axis=1) / base.to_numpy()[boot_index].mean(axis=1)
|
||||
rows.append(
|
||||
{
|
||||
"model": model,
|
||||
"horizon": horizon,
|
||||
"forecasts": len(g),
|
||||
"windows": span / horizon,
|
||||
"crps": g["crps"].mean(),
|
||||
"skill": 1 - m.mean() / base.mean(),
|
||||
"skill_lo": np.quantile(boot, 0.05),
|
||||
"skill_hi": np.quantile(boot, 0.95),
|
||||
**{f"cov{c:.0%}": g[f"in{c:.0%}"].mean() for c in COVERAGES},
|
||||
"mean_pit": g["pit"].mean(),
|
||||
}
|
||||
)
|
||||
return pd.DataFrame(rows)
|
||||
|
||||
|
||||
def _block_bootstrap_indices(n, block, n_boot, rng) -> np.ndarray:
|
||||
"""Circular block bootstrap, so the first and last origins aren't under-sampled."""
|
||||
n_blocks = int(np.ceil(n / block))
|
||||
starts = rng.integers(0, n, size=(n_boot, n_blocks))
|
||||
return ((starts[:, :, None] + np.arange(block)) % n).reshape(n_boot, -1)[:, :n]
|
||||
|
||||
|
||||
def format_summary(summary: pd.DataFrame) -> str:
|
||||
table = pd.DataFrame(
|
||||
{
|
||||
"model": summary["model"],
|
||||
"horizon": summary["horizon"].map(horizon_label),
|
||||
"windows": summary["windows"].map("{:.1f}".format),
|
||||
"crps": summary["crps"].map("{:.3f}".format),
|
||||
"skill vs rw [90%]": [
|
||||
f"{s:+.0%} [{lo:+.0%}, {hi:+.0%}]"
|
||||
for s, lo, hi in zip(summary.skill, summary.skill_lo, summary.skill_hi, strict=True)
|
||||
],
|
||||
**{f"in {c:.0%}": summary[f"cov{c:.0%}"].map("{:.0%}".format) for c in COVERAGES},
|
||||
"mean pit": summary["mean_pit"].map("{:.2f}".format),
|
||||
}
|
||||
)
|
||||
return table.to_string(index=False)
|
||||
|
||||
|
||||
def horizon_label(days: int) -> str:
|
||||
if days < 365:
|
||||
return f"{round(days / 30.4)}mo"
|
||||
return f"{round(days / 365)}y"
|
||||
@@ -0,0 +1,76 @@
|
||||
"""
|
||||
The one output format every model produces, and how it is scored.
|
||||
|
||||
A forecast is a set of quantiles of the natural-log price at each horizon.
|
||||
Quantiles work for any model (closed-form, simulated, bootstrapped, mixtures)
|
||||
and make scoring simple. Working in log price makes errors relative: a CRPS of
|
||||
0.1 is roughly "typically 10% off", in 2013 or in 2026.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from scipy.stats import norm
|
||||
|
||||
N_LEVELS = 100
|
||||
# Midpoints of 100 equal-probability bins: 0.005, 0.015, ..., 0.995.
|
||||
LEVELS = (np.arange(N_LEVELS) + 0.5) / N_LEVELS
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class Forecast:
|
||||
origin: pd.Timestamp
|
||||
horizons: np.ndarray # days after origin, shape (H,)
|
||||
log_quantiles: np.ndarray # log price at LEVELS, shape (H, N_LEVELS), rows nondecreasing
|
||||
|
||||
@classmethod
|
||||
def normal(cls, origin, horizons, mean, sd) -> "Forecast":
|
||||
"""Normal distribution in log price (i.e. lognormal price) at each horizon."""
|
||||
mean = np.broadcast_to(np.asarray(mean, dtype=float), np.shape(horizons))
|
||||
sd = np.broadcast_to(np.asarray(sd, dtype=float), np.shape(horizons))
|
||||
q = mean[:, None] + sd[:, None] * norm.ppf(LEVELS)[None, :]
|
||||
return cls(pd.Timestamp(origin), np.asarray(horizons), q)
|
||||
|
||||
@classmethod
|
||||
def from_samples(cls, origin, horizons, samples) -> "Forecast":
|
||||
"""Empirical quantiles of simulated log prices, shape (n_samples, H)."""
|
||||
q = np.quantile(np.asarray(samples), LEVELS, axis=0).T
|
||||
return cls(pd.Timestamp(origin), np.asarray(horizons), q)
|
||||
|
||||
@property
|
||||
def dates(self) -> pd.DatetimeIndex:
|
||||
return self.origin + pd.to_timedelta(self.horizons, unit="D")
|
||||
|
||||
def quantile(self, level: float) -> np.ndarray:
|
||||
"""Log price at an arbitrary level, interpolated between grid levels."""
|
||||
return np.array([np.interp(level, LEVELS, row) for row in self.log_quantiles])
|
||||
|
||||
def interval(self, coverage: float) -> tuple[np.ndarray, np.ndarray]:
|
||||
"""Central interval in log price holding `coverage` probability."""
|
||||
tail = (1 - coverage) / 2
|
||||
return self.quantile(tail), self.quantile(1 - tail)
|
||||
|
||||
|
||||
def crps(log_quantiles: np.ndarray, outcome: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Continuous ranked probability score, from quantiles, in log-price units.
|
||||
|
||||
CRPS is twice the pinball loss integrated over all quantile levels; the
|
||||
quantile grid gives the integral directly. It rewards sharpness and
|
||||
calibration together and has no free parameters to game. Lower is better.
|
||||
"""
|
||||
outcome = np.asarray(outcome, dtype=float)
|
||||
u = outcome[..., None] - log_quantiles
|
||||
pinball = u * (LEVELS - (u < 0))
|
||||
return 2 * pinball.mean(axis=-1)
|
||||
|
||||
|
||||
def pit(log_quantiles: np.ndarray, outcome: np.ndarray) -> np.ndarray:
|
||||
"""Probability integral transform: forecast CDF evaluated at the outcome."""
|
||||
return np.array(
|
||||
[
|
||||
np.interp(y, q, LEVELS, left=0.0, right=1.0)
|
||||
for q, y in zip(np.atleast_2d(log_quantiles), np.atleast_1d(outcome), strict=True)
|
||||
]
|
||||
)
|
||||
@@ -0,0 +1,34 @@
|
||||
"""Halving calendar and position within the halving cycle."""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
GENESIS = pd.Timestamp("2009-01-03")
|
||||
|
||||
# Block heights 210k, 420k, 630k, 840k (UTC dates).
|
||||
HALVINGS = pd.DatetimeIndex(["2012-11-28", "2016-07-09", "2020-05-11", "2024-04-20"])
|
||||
|
||||
# Later halvings are projected at the length of the last cycle. Block times drift
|
||||
# by weeks per cycle, which is noise at the resolution this is used.
|
||||
_LAST_CYCLE = HALVINGS[-1] - HALVINGS[-2]
|
||||
_PROJECTED = pd.DatetimeIndex([HALVINGS[-1] + k * _LAST_CYCLE for k in range(1, 6)])
|
||||
|
||||
# Genesis starts cycle 0.
|
||||
CYCLE_STARTS = pd.DatetimeIndex([GENESIS]).append(HALVINGS).append(_PROJECTED)
|
||||
|
||||
|
||||
def cycle_position(dates) -> tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
For each date, return (cycle index, days since that cycle began).
|
||||
|
||||
Cycle 0 runs from genesis to the first halving; a halving day is day 0 of
|
||||
the cycle it starts.
|
||||
"""
|
||||
dates = pd.DatetimeIndex(dates)
|
||||
if (dates < GENESIS).any():
|
||||
raise ValueError("date before genesis")
|
||||
if (dates >= CYCLE_STARTS[-1]).any():
|
||||
raise ValueError("date beyond projected halvings")
|
||||
index = CYCLE_STARTS.searchsorted(dates, side="right") - 1
|
||||
days = (dates - CYCLE_STARTS[index]).days
|
||||
return np.asarray(index), np.asarray(days)
|
||||
@@ -0,0 +1,15 @@
|
||||
"""
|
||||
Candidate models.
|
||||
|
||||
A model is any object with a `name` and a
|
||||
`forecast(history: pd.DataFrame, horizons: np.ndarray) -> Forecast` method.
|
||||
`history` holds every row up to and including the forecast origin and nothing
|
||||
after it; the harness guarantees that, so models can use all of it freely.
|
||||
"""
|
||||
|
||||
from .baselines import DriftRandomWalk, RandomWalk
|
||||
from .cycle import CycleModel
|
||||
|
||||
# Order is fixed: it sets each model's colour in every chart.
|
||||
MODELS = {m.name: m for m in (RandomWalk(), DriftRandomWalk(), CycleModel())}
|
||||
BASELINE = RandomWalk.name
|
||||
@@ -0,0 +1,47 @@
|
||||
"""Reference forecasts every other model has to beat."""
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import ClassVar
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from ..data import log_returns
|
||||
from ..forecast import Forecast
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class RandomWalk:
|
||||
"""
|
||||
Zero-drift random walk in log price: "it stays about here, give or take".
|
||||
|
||||
Volatility is the trailing standard deviation of daily log returns.
|
||||
"""
|
||||
|
||||
name: ClassVar[str] = "random_walk"
|
||||
vol_window: int = 365
|
||||
|
||||
def forecast(self, history: pd.DataFrame, horizons: np.ndarray) -> Forecast:
|
||||
sigma = log_returns(history).iloc[-self.vol_window :].std()
|
||||
mean = np.log(history["close"].iloc[-1])
|
||||
return Forecast.normal(history.index[-1], horizons, mean, sigma * np.sqrt(horizons))
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class DriftRandomWalk:
|
||||
"""
|
||||
Random walk whose drift is the mean daily log return over the trailing
|
||||
`drift_window` days (one halving cycle by default): "it keeps doing what it
|
||||
did last cycle".
|
||||
"""
|
||||
|
||||
name: ClassVar[str] = "drift_rw"
|
||||
drift_window: int = 1460
|
||||
vol_window: int = 365
|
||||
|
||||
def forecast(self, history: pd.DataFrame, horizons: np.ndarray) -> Forecast:
|
||||
returns = log_returns(history)
|
||||
mu = returns.iloc[-self.drift_window :].mean()
|
||||
sigma = returns.iloc[-self.vol_window :].std()
|
||||
mean = np.log(history["close"].iloc[-1]) + mu * horizons
|
||||
return Forecast.normal(history.index[-1], horizons, mean, sigma * np.sqrt(horizons))
|
||||
@@ -0,0 +1,73 @@
|
||||
"""The 2024 model, distilled."""
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import ClassVar
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from ..data import log_returns
|
||||
from ..forecast import Forecast
|
||||
from ..halving import cycle_position
|
||||
|
||||
# Longer than any cycle so far (the longest, cycle 0, is 1425 days).
|
||||
MAX_CYCLE_DAYS = 1500
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class CycleModel:
|
||||
"""
|
||||
Expected return depends on how many days it has been since the last halving.
|
||||
|
||||
The drift for day d of the cycle is a weighted mean of the daily log returns
|
||||
observed around day d of every past cycle. Of the 2024 model's ~2000 lines,
|
||||
this idea did all the work. It differs from that model in two ways:
|
||||
|
||||
- Neighbouring cycle days are pooled with a Gaussian kernel. The 2024 model
|
||||
averaged each day separately and then took a rolling mean.
|
||||
- Past cycles are down-weighted, halving each `recency_half_life` cycles, so
|
||||
the 10-100x cycles of 2011-2017 don't set the level. The 2024 model
|
||||
averaged all cycles equally, then scaled by ~0.7; it overshot the
|
||||
2025 peak by ~60%. (This is the idea on the old `tuning-b` branch.)
|
||||
|
||||
Where the data is thin, the drift shrinks toward the overall weighted mean,
|
||||
as if `prior_days` extra observations sat at that value. Noise is a
|
||||
constant-volatility random walk, so the distribution is closed-form.
|
||||
"""
|
||||
|
||||
name: ClassVar[str] = "cycle"
|
||||
bandwidth_days: float = 30.0
|
||||
recency_half_life: float = 1.0
|
||||
prior_days: float = 10.0
|
||||
vol_window: int = 365
|
||||
|
||||
def drift_by_cycle_day(self, history: pd.DataFrame) -> np.ndarray:
|
||||
"""Expected daily log return for each day of the cycle, shape (MAX_CYCLE_DAYS,)."""
|
||||
returns = log_returns(history)
|
||||
cycle, day = cycle_position(returns.index)
|
||||
current_cycle = cycle_position(history.index[-1:])[0][0]
|
||||
weight = 0.5 ** ((current_cycle - cycle) / self.recency_half_life)
|
||||
|
||||
sum_wr = np.bincount(day, weights=weight * returns.values, minlength=MAX_CYCLE_DAYS)
|
||||
sum_w = np.bincount(day, weights=weight, minlength=MAX_CYCLE_DAYS)
|
||||
|
||||
# Peak-1 kernel, so smoothed weights count (recency-weighted) days of data.
|
||||
half_width = int(np.ceil(4 * self.bandwidth_days))
|
||||
offsets = np.arange(-half_width, half_width + 1)
|
||||
kernel = np.exp(-0.5 * (offsets / self.bandwidth_days) ** 2)
|
||||
smooth_wr = np.convolve(sum_wr, kernel, mode="same")
|
||||
smooth_w = np.convolve(sum_w, kernel, mode="same")
|
||||
|
||||
overall = sum_wr.sum() / sum_w.sum()
|
||||
return (smooth_wr + self.prior_days * overall) / (smooth_w + self.prior_days)
|
||||
|
||||
def forecast(self, history: pd.DataFrame, horizons: np.ndarray) -> Forecast:
|
||||
drift = self.drift_by_cycle_day(history)
|
||||
origin = history.index[-1]
|
||||
future = origin + pd.to_timedelta(np.arange(1, horizons.max() + 1), unit="D")
|
||||
_, future_day = cycle_position(future)
|
||||
cumulative = np.cumsum(drift[future_day])
|
||||
|
||||
sigma = log_returns(history).iloc[-self.vol_window :].std()
|
||||
mean = np.log(history["close"].iloc[-1]) + cumulative[horizons - 1]
|
||||
return Forecast.normal(origin, horizons, mean, sigma * np.sqrt(horizons))
|
||||
@@ -0,0 +1,172 @@
|
||||
"""Charts. One colour per model, fixed by its position in MODELS."""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib
|
||||
|
||||
matplotlib.use("Agg")
|
||||
|
||||
import matplotlib.pyplot as plt # noqa: E402
|
||||
import numpy as np # noqa: E402
|
||||
import pandas as pd # noqa: E402
|
||||
from matplotlib.ticker import FuncFormatter, LogLocator, PercentFormatter # noqa: E402
|
||||
|
||||
from .evaluate import COVERAGES, horizon_label # noqa: E402
|
||||
from .forecast import Forecast # noqa: E402
|
||||
from .halving import CYCLE_STARTS # noqa: E402
|
||||
from .models import BASELINE, MODELS # noqa: E402
|
||||
|
||||
SURFACE = "#fcfcfb"
|
||||
INK = "#0b0b0b"
|
||||
INK_SECONDARY = "#52514e"
|
||||
MUTED = "#898781"
|
||||
GRID = "#e1e0d9"
|
||||
AXIS = "#c3c2b7"
|
||||
SERIES = ["#2a78d6", "#eb6834", "#1baf7a", "#eda100", "#e87ba4", "#008300", "#4a3aa7", "#e34948"]
|
||||
|
||||
plt.rcParams.update(
|
||||
{
|
||||
"figure.facecolor": SURFACE,
|
||||
"axes.facecolor": SURFACE,
|
||||
"savefig.facecolor": SURFACE,
|
||||
"axes.edgecolor": AXIS,
|
||||
"axes.linewidth": 0.8,
|
||||
"axes.labelcolor": INK_SECONDARY,
|
||||
"axes.titlecolor": INK,
|
||||
"axes.titlesize": 11,
|
||||
"axes.titleweight": "semibold",
|
||||
"axes.titlelocation": "left",
|
||||
"axes.spines.top": False,
|
||||
"axes.spines.right": False,
|
||||
"axes.grid": True,
|
||||
"grid.color": GRID,
|
||||
"grid.linewidth": 0.8,
|
||||
"xtick.color": MUTED,
|
||||
"ytick.color": MUTED,
|
||||
"xtick.labelcolor": INK_SECONDARY,
|
||||
"ytick.labelcolor": INK_SECONDARY,
|
||||
"legend.frameon": False,
|
||||
"legend.labelcolor": INK_SECONDARY,
|
||||
"lines.linewidth": 2,
|
||||
"lines.solid_capstyle": "round",
|
||||
"lines.solid_joinstyle": "round",
|
||||
"font.family": "sans-serif",
|
||||
"font.size": 9,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def model_color(name: str) -> str:
|
||||
return SERIES[list(MODELS).index(name) % len(SERIES)]
|
||||
|
||||
|
||||
def price_formatter(x, _=None) -> str:
|
||||
for scale, suffix in ((1e9, "B"), (1e6, "M"), (1e3, "K")):
|
||||
if x >= scale:
|
||||
return f"${x / scale:.3g}{suffix}"
|
||||
return f"${x:.3g}"
|
||||
|
||||
|
||||
def fan_chart(history: pd.DataFrame, forecasts: dict[str, Forecast], path: Path) -> None:
|
||||
"""History plus 50/80/95% intervals, one panel per model on shared axes."""
|
||||
fig, axes = plt.subplots(
|
||||
len(forecasts), 1, figsize=(10, 3.2 * len(forecasts)), sharex=True, sharey=True
|
||||
)
|
||||
axes = np.atleast_1d(axes)
|
||||
shown = history[history.index >= history.index[-1] - pd.Timedelta(days=6 * 365)]
|
||||
for ax, (name, f) in zip(axes, forecasts.items(), strict=True):
|
||||
color = model_color(name)
|
||||
for c in sorted(COVERAGES, reverse=True):
|
||||
lo, hi = f.interval(c)
|
||||
ax.fill_between(f.dates, np.exp(lo), np.exp(hi), color=color, alpha=0.1, lw=0)
|
||||
median = np.exp(f.quantile(0.5))
|
||||
ax.plot(shown.index, shown["close"], color=INK, lw=1.2)
|
||||
ax.plot(f.dates, median, color=color)
|
||||
ax.annotate(
|
||||
f"median {price_formatter(median[-1])}",
|
||||
(f.dates[-1], median[-1]),
|
||||
xytext=(6, 0),
|
||||
textcoords="offset points",
|
||||
va="center",
|
||||
color=INK_SECONDARY,
|
||||
)
|
||||
for start in CYCLE_STARTS:
|
||||
if shown.index[0] <= start <= f.dates[-1]:
|
||||
ax.axvline(start, color=AXIS, lw=0.8, zorder=0)
|
||||
ax.set_yscale("log")
|
||||
ax.yaxis.set_major_locator(LogLocator(base=10, subs=(1, 2, 5)))
|
||||
ax.yaxis.set_major_formatter(FuncFormatter(price_formatter))
|
||||
ax.yaxis.set_minor_formatter(FuncFormatter(lambda *_: ""))
|
||||
ax.set_title(f"{name}: forecast from {f.origin:%Y-%m-%d}")
|
||||
axes[0].fill_between([], [], color=MUTED, alpha=0.3, label="50% interval")
|
||||
axes[0].fill_between([], [], color=MUTED, alpha=0.2, label="80% interval")
|
||||
axes[0].fill_between([], [], color=MUTED, alpha=0.1, label="95% interval")
|
||||
axes[0].legend(loc="upper left")
|
||||
axes[-1].set_xlabel("vertical lines: halvings (future ones projected)", color=MUTED)
|
||||
fig.tight_layout()
|
||||
fig.savefig(path, dpi=150)
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
def skill_chart(summary: pd.DataFrame, path: Path, title: str) -> None:
|
||||
"""CRPS skill vs the random walk, by horizon, with bootstrap intervals."""
|
||||
horizons = sorted(summary["horizon"].unique())
|
||||
x = np.arange(len(horizons))
|
||||
fig, ax = plt.subplots(figsize=(8, 4.5))
|
||||
ax.axhline(0, color=model_color(BASELINE), lw=2, label=BASELINE)
|
||||
for name, g in summary[summary.model != BASELINE].groupby("model", sort=False):
|
||||
g = g.set_index("horizon").reindex(horizons)
|
||||
color = model_color(name)
|
||||
ax.fill_between(x, g.skill_lo, g.skill_hi, color=color, alpha=0.1, lw=0)
|
||||
ax.plot(x, g.skill, color=color, marker="o", ms=6, mec=SURFACE, mew=2, label=name)
|
||||
ax.annotate(
|
||||
name,
|
||||
(x[-1], g.skill.iloc[-1]),
|
||||
xytext=(8, 0),
|
||||
textcoords="offset points",
|
||||
va="center",
|
||||
color=INK_SECONDARY,
|
||||
)
|
||||
ax.set_xticks(x, [horizon_label(h) for h in horizons])
|
||||
ax.set_xlabel("forecast horizon")
|
||||
ax.set_ylabel("CRPS skill vs random walk (higher is better)")
|
||||
ax.yaxis.set_major_formatter(PercentFormatter(1.0, decimals=0))
|
||||
ax.set_title(title)
|
||||
ax.legend(loc="lower left")
|
||||
fig.tight_layout()
|
||||
fig.savefig(path, dpi=150)
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
def calibration_chart(summary: pd.DataFrame, path: Path, title: str) -> None:
|
||||
"""How often each nominal interval contained the outcome, by horizon."""
|
||||
horizons = sorted(summary["horizon"].unique())
|
||||
x = np.arange(len(horizons))
|
||||
fig, axes = plt.subplots(1, len(COVERAGES), figsize=(12, 4), sharey=True)
|
||||
for ax, c in zip(axes, COVERAGES, strict=True):
|
||||
ax.axhline(c, color=INK_SECONDARY, lw=1)
|
||||
ax.annotate(
|
||||
"target", (x[-1], c), xytext=(0, 4), textcoords="offset points", ha="right", color=MUTED
|
||||
)
|
||||
for name, g in summary.groupby("model", sort=False):
|
||||
g = g.set_index("horizon").reindex(horizons)
|
||||
ax.plot(
|
||||
x,
|
||||
g[f"cov{c:.0%}"],
|
||||
color=model_color(name),
|
||||
marker="o",
|
||||
ms=6,
|
||||
mec=SURFACE,
|
||||
mew=2,
|
||||
label=name,
|
||||
)
|
||||
ax.set_xticks(x, [horizon_label(h) for h in horizons])
|
||||
ax.set_title(f"{c:.0%} interval")
|
||||
ax.set_ylim(0, 1.02)
|
||||
ax.yaxis.set_major_formatter(PercentFormatter(1.0, decimals=0))
|
||||
axes[0].set_ylabel("share of outcomes inside")
|
||||
axes[-1].legend(loc="lower left")
|
||||
fig.suptitle(title, x=0.01, ha="left", color=INK, fontsize=11, fontweight="semibold")
|
||||
fig.tight_layout()
|
||||
fig.savefig(path, dpi=150)
|
||||
plt.close(fig)
|
||||
@@ -0,0 +1,667 @@
|
||||
date,close
|
||||
2024-11-27,95951.19
|
||||
2024-11-28,95665.53
|
||||
2024-11-29,97490.56
|
||||
2024-11-30,96465.42
|
||||
2024-12-01,97263.18
|
||||
2024-12-02,95862.89
|
||||
2024-12-03,95924.52
|
||||
2024-12-04,98746.24
|
||||
2024-12-05,97044.23
|
||||
2024-12-06,99891.35
|
||||
2024-12-07,99929.32
|
||||
2024-12-08,101174.99
|
||||
2024-12-09,97324.81
|
||||
2024-12-10,96660.76
|
||||
2024-12-11,101202.11
|
||||
2024-12-12,100030.47
|
||||
2024-12-13,101428.75
|
||||
2024-12-14,101399.99
|
||||
2024-12-15,104447.76
|
||||
2024-12-16,106099.81
|
||||
2024-12-17,106136.99
|
||||
2024-12-18,100150.73
|
||||
2024-12-19,97372.21
|
||||
2024-12-20,97765.0
|
||||
2024-12-21,97230.08
|
||||
2024-12-22,95087.75
|
||||
2024-12-23,94764.56
|
||||
2024-12-24,98594.47
|
||||
2024-12-25,99346.28
|
||||
2024-12-26,95669.49
|
||||
2024-12-27,94171.89
|
||||
2024-12-28,95130.82
|
||||
2024-12-29,93563.35
|
||||
2024-12-30,92620.71
|
||||
2024-12-31,93354.22
|
||||
2025-01-01,94383.59
|
||||
2025-01-02,96903.19
|
||||
2025-01-03,98136.51
|
||||
2025-01-04,98209.85
|
||||
2025-01-05,98345.33
|
||||
2025-01-06,102279.41
|
||||
2025-01-07,96941.98
|
||||
2025-01-08,95036.63
|
||||
2025-01-09,92547.44
|
||||
2025-01-10,94701.18
|
||||
2025-01-11,94565.02
|
||||
2025-01-12,94509.62
|
||||
2025-01-13,94506.45
|
||||
2025-01-14,96534.96
|
||||
2025-01-15,100510.23
|
||||
2025-01-16,99981.78
|
||||
2025-01-17,104107.0
|
||||
2025-01-18,104435.0
|
||||
2025-01-19,101211.13
|
||||
2025-01-20,102145.43
|
||||
2025-01-21,106159.26
|
||||
2025-01-22,103667.11
|
||||
2025-01-23,103926.36
|
||||
2025-01-24,104850.27
|
||||
2025-01-25,104733.56
|
||||
2025-01-26,102563.0
|
||||
2025-01-27,102062.42
|
||||
2025-01-28,101290.0
|
||||
2025-01-29,103747.25
|
||||
2025-01-30,104742.64
|
||||
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|
||||
2026-03-08,65970.56
|
||||
2026-03-09,68432.17
|
||||
2026-03-10,69960.83
|
||||
2026-03-11,70208.0
|
||||
2026-03-12,70531.56
|
||||
2026-03-13,70944.33
|
||||
2026-03-14,71232.02
|
||||
2026-03-15,72830.01
|
||||
2026-03-16,74886.47
|
||||
2026-03-17,73934.11
|
||||
2026-03-18,71245.02
|
||||
2026-03-19,69918.3
|
||||
2026-03-20,70497.01
|
||||
2026-03-21,68912.02
|
||||
2026-03-22,67844.94
|
||||
2026-03-23,70874.23
|
||||
2026-03-24,70533.49
|
||||
2026-03-25,71301.53
|
||||
2026-03-26,68769.02
|
||||
2026-03-27,66353.34
|
||||
2026-03-28,66320.15
|
||||
2026-03-29,65956.94
|
||||
2026-03-30,66737.18
|
||||
2026-03-31,68221.85
|
||||
2026-04-01,68112.35
|
||||
2026-04-02,66894.48
|
||||
2026-04-03,66959.99
|
||||
2026-04-04,67291.73
|
||||
2026-04-05,69005.0
|
||||
2026-04-06,68853.61
|
||||
2026-04-07,71910.2
|
||||
2026-04-08,71085.99
|
||||
2026-04-09,71798.01
|
||||
2026-04-10,72997.88
|
||||
2026-04-11,73086.0
|
||||
2026-04-12,70755.35
|
||||
2026-04-13,74446.0
|
||||
2026-04-14,74179.04
|
||||
2026-04-15,74836.31
|
||||
2026-04-16,75163.09
|
||||
2026-04-17,77098.01
|
||||
2026-04-18,75736.82
|
||||
2026-04-19,73823.14
|
||||
2026-04-20,75864.86
|
||||
2026-04-21,76348.58
|
||||
2026-04-22,78208.52
|
||||
2026-04-23,78281.32
|
||||
2026-04-24,77461.8
|
||||
2026-04-25,77650.72
|
||||
2026-04-26,78673.83
|
||||
2026-04-27,77364.42
|
||||
2026-04-28,76317.99
|
||||
2026-04-29,75752.88
|
||||
2026-04-30,76305.78
|
||||
2026-05-01,78234.05
|
||||
2026-05-02,78682.31
|
||||
2026-05-03,78558.89
|
||||
2026-05-04,79852.37
|
||||
2026-05-05,80907.72
|
||||
2026-05-06,81438.1
|
||||
2026-05-07,80005.38
|
||||
2026-05-08,80188.82
|
||||
2026-05-09,80655.96
|
||||
2026-05-10,82199.99
|
||||
2026-05-11,81727.06
|
||||
2026-05-12,80484.72
|
||||
2026-05-13,79288.26
|
||||
2026-05-14,81079.39
|
||||
2026-05-15,79058.51
|
||||
2026-05-16,78105.45
|
||||
2026-05-17,77407.59
|
||||
2026-05-18,76943.98
|
||||
2026-05-19,76767.48
|
||||
2026-05-20,77475.99
|
||||
2026-05-21,77547.62
|
||||
2026-05-22,75443.91
|
||||
2026-05-23,76650.0
|
||||
2026-05-24,76975.99
|
||||
2026-05-25,77249.07
|
||||
2026-05-26,75826.1
|
||||
2026-05-27,74315.98
|
||||
2026-05-28,73512.56
|
||||
2026-05-29,73366.77
|
||||
2026-05-30,73770.69
|
||||
2026-05-31,73575.17
|
||||
2026-06-01,71314.42
|
||||
2026-06-02,66658.35
|
||||
2026-06-03,64040.0
|
||||
2026-06-04,63806.02
|
||||
2026-06-05,61032.0
|
||||
2026-06-06,60850.48
|
||||
2026-06-07,63302.73
|
||||
2026-06-08,63062.98
|
||||
2026-06-09,61685.8
|
||||
2026-06-10,61449.46
|
||||
2026-06-11,63563.42
|
||||
2026-06-12,63538.43
|
||||
2026-06-13,64429.99
|
||||
2026-06-14,65706.62
|
||||
2026-06-15,66276.8
|
||||
2026-06-16,65616.64
|
||||
2026-06-17,64446.04
|
||||
2026-06-18,62879.01
|
||||
2026-06-19,63475.2
|
||||
2026-06-20,64222.99
|
||||
2026-06-21,63235.63
|
||||
2026-06-22,63950.61
|
||||
2026-06-23,62644.86
|
||||
2026-06-24,60983.13
|
||||
2026-06-25,59703.65
|
||||
2026-06-26,60002.53
|
||||
2026-06-27,59934.84
|
||||
2026-06-28,59474.01
|
||||
2026-06-29,60162.73
|
||||
2026-06-30,58523.93
|
||||
2026-07-01,59961.45
|
||||
2026-07-02,61484.02
|
||||
2026-07-03,62520.22
|
||||
2026-07-04,63086.45
|
||||
2026-07-05,63580.44
|
||||
2026-07-06,64001.84
|
||||
2026-07-07,63323.27
|
||||
2026-07-08,62237.7
|
||||
2026-07-09,63175.25
|
||||
2026-07-10,64128.49
|
||||
2026-07-11,63773.04
|
||||
2026-07-12,63740.32
|
||||
2026-07-13,62264.94
|
||||
2026-07-14,64988.64
|
||||
2026-07-15,64716.63
|
||||
2026-07-16,63770.6
|
||||
2026-07-17,63894.0
|
||||
2026-07-18,64796.36
|
||||
2026-07-19,64681.78
|
||||
2026-07-20,65213.05
|
||||
2026-07-21,66516.18
|
||||
2026-07-22,66086.61
|
||||
2026-07-23,65051.23
|
||||
2026-07-24,64083.32
|
||||
2026-07-25,64295.61
|
||||
2026-07-26,65341.07
|
||||
2026-07-27,63694.43
|
||||
2026-07-28,63847.12
|
||||
2026-07-29,63896.14
|
||||
2026-07-30,64721.9
|
||||
2026-07-31,62825.9
|
||||
2026-08-01,62764.2
|
||||
2026-08-02,63499.49
|
||||
2026-08-03,63466.51
|
||||
2026-08-04,64050.51
|
||||
2026-08-05,64603.03
|
||||
2026-08-06,64267.3
|
||||
2026-08-07,64891.61
|
||||
2026-08-08,64908.72
|
||||
2026-08-09,64848.69
|
||||
2026-08-10,63911.88
|
||||
2026-08-11,63531.75
|
||||
2026-08-12,63411.72
|
||||
2026-08-13,63425.35
|
||||
2026-08-14,62975.19
|
||||
2026-08-15,63018.75
|
||||
2026-08-16,62836.66
|
||||
2026-08-17,64484.18
|
||||
2026-08-18,64681.33
|
||||
2026-08-19,69300.01
|
||||
2026-08-20,73011.87
|
||||
2026-08-21,78325.54
|
||||
2026-08-22,77054.44
|
||||
2026-08-23,77729.36
|
||||
2026-08-24,78981.59
|
||||
2026-08-25,78526.8
|
||||
2026-08-26,79026.18
|
||||
2026-08-27,80275.34
|
||||
2026-08-28,77839.19
|
||||
2026-08-29,78233.93
|
||||
2026-08-30,77665.14
|
||||
2026-08-31,78562.74
|
||||
2026-09-01,77398.69
|
||||
2026-09-02,77307.36
|
||||
2026-09-03,81263.99
|
||||
2026-09-04,79675.12
|
||||
2026-09-05,79831.57
|
||||
2026-09-06,80339.13
|
||||
2026-09-07,79091.97
|
||||
2026-09-08,78447.11
|
||||
2026-09-09,78283.98
|
||||
2026-09-10,76536.55
|
||||
2026-09-11,77208.55
|
||||
2026-09-12,77262.85
|
||||
2026-09-13,76799.85
|
||||
2026-09-14,78175.01
|
||||
2026-09-15,75584.17
|
||||
2026-09-16,76144.99
|
||||
2026-09-17,76348.74
|
||||
2026-09-18,80875.04
|
||||
2026-09-19,81233.91
|
||||
2026-09-20,81159.64
|
||||
2026-09-21,86594.94
|
||||
2026-09-22,86198.05
|
||||
2026-09-23,84378.31
|
||||
|
|
Before Width: | Height: | Size: 783 KiB After Width: | Height: | Size: 783 KiB |
Generated
+27
@@ -0,0 +1,27 @@
|
||||
{
|
||||
"nodes": {
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1790178388,
|
||||
"narHash": "sha256-kK3t7gwoz4Nx8RF46cs1Xz/skKNcK7y3KYP+gGqX6W8=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "00455b0a3690d3f5dc61e9aef4277dc86235b73f",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"owner": "NixOS",
|
||||
"ref": "nixpkgs-unstable",
|
||||
"repo": "nixpkgs",
|
||||
"type": "github"
|
||||
}
|
||||
},
|
||||
"root": {
|
||||
"inputs": {
|
||||
"nixpkgs": "nixpkgs"
|
||||
}
|
||||
}
|
||||
},
|
||||
"root": "root",
|
||||
"version": 7
|
||||
}
|
||||
@@ -0,0 +1,36 @@
|
||||
{
|
||||
description = "Bitcoin price model";
|
||||
|
||||
inputs.nixpkgs.url = "github:NixOS/nixpkgs/nixpkgs-unstable";
|
||||
|
||||
outputs =
|
||||
{ self, nixpkgs }:
|
||||
let
|
||||
systems = [
|
||||
"aarch64-darwin"
|
||||
"x86_64-darwin"
|
||||
"aarch64-linux"
|
||||
"x86_64-linux"
|
||||
];
|
||||
forAllSystems = f: nixpkgs.lib.genAttrs systems (system: f nixpkgs.legacyPackages.${system});
|
||||
in
|
||||
{
|
||||
devShells = forAllSystems (pkgs: {
|
||||
default = pkgs.mkShell {
|
||||
packages = [
|
||||
(pkgs.python313.withPackages (ps: [
|
||||
ps.numpy
|
||||
ps.pandas
|
||||
ps.scipy
|
||||
ps.matplotlib
|
||||
ps.pytest
|
||||
]))
|
||||
pkgs.ruff
|
||||
pkgs.just
|
||||
];
|
||||
};
|
||||
});
|
||||
|
||||
formatter = forAllSystems (pkgs: pkgs.nixfmt);
|
||||
};
|
||||
}
|
||||
@@ -1,11 +1,34 @@
|
||||
run *name:
|
||||
python3 ./model.py -n "{{ name }}"
|
||||
# List recipes
|
||||
default:
|
||||
@just --list
|
||||
|
||||
# Fetch new daily prices from Coinbase
|
||||
update:
|
||||
python -m btcmodel update
|
||||
|
||||
# Score models on development data (outcomes up to the dev cutoff)
|
||||
backtest *args:
|
||||
python -m btcmodel {{ args }} backtest
|
||||
|
||||
# Score models on held-out outcomes; run sparingly, never while tuning
|
||||
holdout *args:
|
||||
python -m btcmodel {{ args }} backtest --holdout
|
||||
|
||||
# Forecast from the latest price
|
||||
forecast *args:
|
||||
python -m btcmodel {{ args }} forecast
|
||||
|
||||
test:
|
||||
pytest -q
|
||||
|
||||
fmt:
|
||||
black ./*.py
|
||||
ruff format .
|
||||
ruff check --fix --select I .
|
||||
nix fmt flake.nix
|
||||
|
||||
lint:
|
||||
ruff check ./model.py
|
||||
ruff check .
|
||||
ruff format --check .
|
||||
|
||||
clean:
|
||||
rm -rf output
|
||||
|
||||
Generated
-734
@@ -1,734 +0,0 @@
|
||||
# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "contourpy"
|
||||
version = "1.3.1"
|
||||
description = "Python library for calculating contours of 2D quadrilateral grids"
|
||||
optional = false
|
||||
python-versions = ">=3.10"
|
||||
files = [
|
||||
{file = "contourpy-1.3.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a045f341a77b77e1c5de31e74e966537bba9f3c4099b35bf4c2e3939dd54cdab"},
|
||||
{file = "contourpy-1.3.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:500360b77259914f7805af7462e41f9cb7ca92ad38e9f94d6c8641b089338124"},
|
||||
{file = "contourpy-1.3.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2f926efda994cdf3c8d3fdb40b9962f86edbc4457e739277b961eced3d0b4c1"},
|
||||
{file = "contourpy-1.3.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:adce39d67c0edf383647a3a007de0a45fd1b08dedaa5318404f1a73059c2512b"},
|
||||
{file = "contourpy-1.3.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:abbb49fb7dac584e5abc6636b7b2a7227111c4f771005853e7d25176daaf8453"},
|
||||
{file = "contourpy-1.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a0cffcbede75c059f535725c1680dfb17b6ba8753f0c74b14e6a9c68c29d7ea3"},
|
||||
{file = "contourpy-1.3.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:ab29962927945d89d9b293eabd0d59aea28d887d4f3be6c22deaefbb938a7277"},
|
||||
{file = "contourpy-1.3.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:974d8145f8ca354498005b5b981165b74a195abfae9a8129df3e56771961d595"},
|
||||
{file = "contourpy-1.3.1-cp310-cp310-win32.whl", hash = "sha256:ac4578ac281983f63b400f7fe6c101bedc10651650eef012be1ccffcbacf3697"},
|
||||
{file = "contourpy-1.3.1-cp310-cp310-win_amd64.whl", hash = "sha256:174e758c66bbc1c8576992cec9599ce8b6672b741b5d336b5c74e35ac382b18e"},
|
||||
{file = "contourpy-1.3.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3e8b974d8db2c5610fb4e76307e265de0edb655ae8169e8b21f41807ccbeec4b"},
|
||||
{file = "contourpy-1.3.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:20914c8c973f41456337652a6eeca26d2148aa96dd7ac323b74516988bea89fc"},
|
||||
{file = "contourpy-1.3.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:19d40d37c1c3a4961b4619dd9d77b12124a453cc3d02bb31a07d58ef684d3d86"},
|
||||
{file = "contourpy-1.3.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:113231fe3825ebf6f15eaa8bc1f5b0ddc19d42b733345eae0934cb291beb88b6"},
|
||||
{file = "contourpy-1.3.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4dbbc03a40f916a8420e420d63e96a1258d3d1b58cbdfd8d1f07b49fcbd38e85"},
|
||||
{file = "contourpy-1.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a04ecd68acbd77fa2d39723ceca4c3197cb2969633836ced1bea14e219d077c"},
|
||||
{file = "contourpy-1.3.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c414fc1ed8ee1dbd5da626cf3710c6013d3d27456651d156711fa24f24bd1291"},
|
||||
{file = "contourpy-1.3.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:31c1b55c1f34f80557d3830d3dd93ba722ce7e33a0b472cba0ec3b6535684d8f"},
|
||||
{file = "contourpy-1.3.1-cp311-cp311-win32.whl", hash = "sha256:f611e628ef06670df83fce17805c344710ca5cde01edfdc72751311da8585375"},
|
||||
{file = "contourpy-1.3.1-cp311-cp311-win_amd64.whl", hash = "sha256:b2bdca22a27e35f16794cf585832e542123296b4687f9fd96822db6bae17bfc9"},
|
||||
{file = "contourpy-1.3.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:0ffa84be8e0bd33410b17189f7164c3589c229ce5db85798076a3fa136d0e509"},
|
||||
{file = "contourpy-1.3.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:805617228ba7e2cbbfb6c503858e626ab528ac2a32a04a2fe88ffaf6b02c32bc"},
|
||||
{file = "contourpy-1.3.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ade08d343436a94e633db932e7e8407fe7de8083967962b46bdfc1b0ced39454"},
|
||||
{file = "contourpy-1.3.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:47734d7073fb4590b4a40122b35917cd77be5722d80683b249dac1de266aac80"},
|
||||
{file = "contourpy-1.3.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2ba94a401342fc0f8b948e57d977557fbf4d515f03c67682dd5c6191cb2d16ec"},
|
||||
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||||
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||||
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||||
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||||
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|
||||
|
||||
[package.extras]
|
||||
docs = ["furo", "olefile", "sphinx (>=8.1)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
|
||||
fpx = ["olefile"]
|
||||
mic = ["olefile"]
|
||||
tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
|
||||
typing = ["typing-extensions"]
|
||||
xmp = ["defusedxml"]
|
||||
|
||||
[[package]]
|
||||
name = "pyparsing"
|
||||
version = "3.2.0"
|
||||
description = "pyparsing module - Classes and methods to define and execute parsing grammars"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "pyparsing-3.2.0-py3-none-any.whl", hash = "sha256:93d9577b88da0bbea8cc8334ee8b918ed014968fd2ec383e868fb8afb1ccef84"},
|
||||
{file = "pyparsing-3.2.0.tar.gz", hash = "sha256:cbf74e27246d595d9a74b186b810f6fbb86726dbf3b9532efb343f6d7294fe9c"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
diagrams = ["jinja2", "railroad-diagrams"]
|
||||
|
||||
[[package]]
|
||||
name = "python-dateutil"
|
||||
version = "2.9.0.post0"
|
||||
description = "Extensions to the standard Python datetime module"
|
||||
optional = false
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
|
||||
files = [
|
||||
{file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
|
||||
{file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
six = ">=1.5"
|
||||
|
||||
[[package]]
|
||||
name = "pytz"
|
||||
version = "2024.2"
|
||||
description = "World timezone definitions, modern and historical"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "pytz-2024.2-py2.py3-none-any.whl", hash = "sha256:31c7c1817eb7fae7ca4b8c7ee50c72f93aa2dd863de768e1ef4245d426aa0725"},
|
||||
{file = "pytz-2024.2.tar.gz", hash = "sha256:2aa355083c50a0f93fa581709deac0c9ad65cca8a9e9beac660adcbd493c798a"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "scipy"
|
||||
version = "1.14.1"
|
||||
description = "Fundamental algorithms for scientific computing in Python"
|
||||
optional = false
|
||||
python-versions = ">=3.10"
|
||||
files = [
|
||||
{file = "scipy-1.14.1-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:b28d2ca4add7ac16ae8bb6632a3c86e4b9e4d52d3e34267f6e1b0c1f8d87e389"},
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||||
{file = "scipy-1.14.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:d0d2821003174de06b69e58cef2316a6622b60ee613121199cb2852a873f8cf3"},
|
||||
{file = "scipy-1.14.1-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:8bddf15838ba768bb5f5083c1ea012d64c9a444e16192762bd858f1e126196d0"},
|
||||
{file = "scipy-1.14.1-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:97c5dddd5932bd2a1a31c927ba5e1463a53b87ca96b5c9bdf5dfd6096e27efc3"},
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||||
{file = "scipy-1.14.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2ff0a7e01e422c15739ecd64432743cf7aae2b03f3084288f399affcefe5222d"},
|
||||
{file = "scipy-1.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e32dced201274bf96899e6491d9ba3e9a5f6b336708656466ad0522d8528f69"},
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||||
{file = "scipy-1.14.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8426251ad1e4ad903a4514712d2fa8fdd5382c978010d1c6f5f37ef286a713ad"},
|
||||
{file = "scipy-1.14.1-cp310-cp310-win_amd64.whl", hash = "sha256:a49f6ed96f83966f576b33a44257d869756df6cf1ef4934f59dd58b25e0327e5"},
|
||||
{file = "scipy-1.14.1-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:2da0469a4ef0ecd3693761acbdc20f2fdeafb69e6819cc081308cc978153c675"},
|
||||
{file = "scipy-1.14.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:c0ee987efa6737242745f347835da2cc5bb9f1b42996a4d97d5c7ff7928cb6f2"},
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||||
{file = "scipy-1.14.1-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:3a1b111fac6baec1c1d92f27e76511c9e7218f1695d61b59e05e0fe04dc59617"},
|
||||
{file = "scipy-1.14.1-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:8475230e55549ab3f207bff11ebfc91c805dc3463ef62eda3ccf593254524ce8"},
|
||||
{file = "scipy-1.14.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:278266012eb69f4a720827bdd2dc54b2271c97d84255b2faaa8f161a158c3b37"},
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||||
{file = "scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fef8c87f8abfb884dac04e97824b61299880c43f4ce675dd2cbeadd3c9b466d2"},
|
||||
{file = "scipy-1.14.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:b05d43735bb2f07d689f56f7b474788a13ed8adc484a85aa65c0fd931cf9ccd2"},
|
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{file = "scipy-1.14.1-cp311-cp311-win_amd64.whl", hash = "sha256:716e389b694c4bb564b4fc0c51bc84d381735e0d39d3f26ec1af2556ec6aad94"},
|
||||
{file = "scipy-1.14.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:631f07b3734d34aced009aaf6fedfd0eb3498a97e581c3b1e5f14a04164a456d"},
|
||||
{file = "scipy-1.14.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:af29a935803cc707ab2ed7791c44288a682f9c8107bc00f0eccc4f92c08d6e07"},
|
||||
{file = "scipy-1.14.1-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:2843f2d527d9eebec9a43e6b406fb7266f3af25a751aa91d62ff416f54170bc5"},
|
||||
{file = "scipy-1.14.1-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:eb58ca0abd96911932f688528977858681a59d61a7ce908ffd355957f7025cfc"},
|
||||
{file = "scipy-1.14.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:30ac8812c1d2aab7131a79ba62933a2a76f582d5dbbc695192453dae67ad6310"},
|
||||
{file = "scipy-1.14.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f9ea80f2e65bdaa0b7627fb00cbeb2daf163caa015e59b7516395fe3bd1e066"},
|
||||
{file = "scipy-1.14.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:edaf02b82cd7639db00dbff629995ef185c8df4c3ffa71a5562a595765a06ce1"},
|
||||
{file = "scipy-1.14.1-cp312-cp312-win_amd64.whl", hash = "sha256:2ff38e22128e6c03ff73b6bb0f85f897d2362f8c052e3b8ad00532198fbdae3f"},
|
||||
{file = "scipy-1.14.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:1729560c906963fc8389f6aac023739ff3983e727b1a4d87696b7bf108316a79"},
|
||||
{file = "scipy-1.14.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:4079b90df244709e675cdc8b93bfd8a395d59af40b72e339c2287c91860deb8e"},
|
||||
{file = "scipy-1.14.1-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:e0cf28db0f24a38b2a0ca33a85a54852586e43cf6fd876365c86e0657cfe7d73"},
|
||||
{file = "scipy-1.14.1-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:0c2f95de3b04e26f5f3ad5bb05e74ba7f68b837133a4492414b3afd79dfe540e"},
|
||||
{file = "scipy-1.14.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b99722ea48b7ea25e8e015e8341ae74624f72e5f21fc2abd45f3a93266de4c5d"},
|
||||
{file = "scipy-1.14.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5149e3fd2d686e42144a093b206aef01932a0059c2a33ddfa67f5f035bdfe13e"},
|
||||
{file = "scipy-1.14.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e4f5a7c49323533f9103d4dacf4e4f07078f360743dec7f7596949149efeec06"},
|
||||
{file = "scipy-1.14.1-cp313-cp313-win_amd64.whl", hash = "sha256:baff393942b550823bfce952bb62270ee17504d02a1801d7fd0719534dfb9c84"},
|
||||
{file = "scipy-1.14.1.tar.gz", hash = "sha256:5a275584e726026a5699459aa72f828a610821006228e841b94275c4a7c08417"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.23.5,<2.3"
|
||||
|
||||
[package.extras]
|
||||
dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
|
||||
doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
|
||||
test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
|
||||
|
||||
[[package]]
|
||||
name = "seaborn"
|
||||
version = "0.13.2"
|
||||
description = "Statistical data visualization"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987"},
|
||||
{file = "seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
matplotlib = ">=3.4,<3.6.1 || >3.6.1"
|
||||
numpy = ">=1.20,<1.24.0 || >1.24.0"
|
||||
pandas = ">=1.2"
|
||||
|
||||
[package.extras]
|
||||
dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
|
||||
docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
|
||||
stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
|
||||
|
||||
[[package]]
|
||||
name = "six"
|
||||
version = "1.16.0"
|
||||
description = "Python 2 and 3 compatibility utilities"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
|
||||
files = [
|
||||
{file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
|
||||
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2024.2"
|
||||
description = "Provider of IANA time zone data"
|
||||
optional = false
|
||||
python-versions = ">=2"
|
||||
files = [
|
||||
{file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
|
||||
{file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
|
||||
]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.13"
|
||||
content-hash = "376b98659bcfe622090d838b23f142eaa76c6441ace6ca751db716db53d9af63"
|
||||
+20
-14
@@ -1,18 +1,24 @@
|
||||
[tool.poetry]
|
||||
name = "bitcoin-model"
|
||||
version = "0.1.0"
|
||||
description = ""
|
||||
authors = ["Anthropic Claude <[email protected]>", "Sam Fredrickson <[email protected]>"]
|
||||
[project]
|
||||
name = "btcmodel"
|
||||
version = "0.2.0"
|
||||
description = "Probabilistic Bitcoin price model"
|
||||
authors = [
|
||||
{ name = "Anthropic Claude", email = "[email protected]" },
|
||||
{ name = "Sam Fredrickson", email = "[email protected]" },
|
||||
]
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.13"
|
||||
dependencies = ["numpy>=2", "pandas>=3", "scipy>=1.14", "matplotlib>=3.9"]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.13"
|
||||
pandas = "^2.2.3"
|
||||
matplotlib = "^3.9.2"
|
||||
seaborn = "^0.13.2"
|
||||
scipy = "^1.14.1"
|
||||
[dependency-groups]
|
||||
dev = ["pytest", "ruff"]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 100
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
[tool.ruff.lint]
|
||||
select = ["E", "F", "I", "B", "UP"]
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
pythonpath = ["."]
|
||||
|
||||
@@ -0,0 +1,44 @@
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from scipy.stats import norm
|
||||
|
||||
from btcmodel.forecast import Forecast, crps, pit
|
||||
|
||||
|
||||
def normal_crps(mu, sigma, y):
|
||||
"""Closed-form CRPS of N(mu, sigma^2) at y."""
|
||||
z = (y - mu) / sigma
|
||||
return sigma * (z * (2 * norm.cdf(z) - 1) + 2 * norm.pdf(z) - 1 / np.sqrt(np.pi))
|
||||
|
||||
|
||||
def test_crps_matches_closed_form_for_normal():
|
||||
f = Forecast.normal("2020-01-01", np.array([1, 2, 3]), mean=[0.0, 1.0, 2.0], sd=[1.0, 0.5, 2.0])
|
||||
y = np.array([0.3, -0.2, 5.0])
|
||||
expected = normal_crps(np.array([0.0, 1.0, 2.0]), np.array([1.0, 0.5, 2.0]), y)
|
||||
np.testing.assert_allclose(crps(f.log_quantiles, y), expected, rtol=0.02)
|
||||
|
||||
|
||||
def test_crps_prefers_the_right_forecast():
|
||||
y = np.array([0.0])
|
||||
good = Forecast.normal("2020-01-01", np.array([1]), 0.0, 0.1)
|
||||
biased = Forecast.normal("2020-01-01", np.array([1]), 0.5, 0.1)
|
||||
vague = Forecast.normal("2020-01-01", np.array([1]), 0.0, 2.0)
|
||||
assert crps(good.log_quantiles, y) < crps(biased.log_quantiles, y)
|
||||
assert crps(good.log_quantiles, y) < crps(vague.log_quantiles, y)
|
||||
|
||||
|
||||
def test_pit_and_intervals():
|
||||
f = Forecast.normal("2020-01-01", np.array([1, 1, 1]), 0.0, 1.0)
|
||||
np.testing.assert_allclose(
|
||||
pit(f.log_quantiles, [0.0, 1.0, -10.0]), [0.5, 0.841, 0.0], atol=0.01
|
||||
)
|
||||
lo, hi = f.interval(0.95)
|
||||
np.testing.assert_allclose(hi, 1.96, atol=0.01)
|
||||
np.testing.assert_allclose(lo, -1.96, atol=0.01)
|
||||
|
||||
|
||||
def test_from_samples_recovers_quantiles():
|
||||
rng = np.random.default_rng(0)
|
||||
f = Forecast.from_samples("2020-01-01", np.array([10]), rng.normal(0, 1, size=(200_000, 1)))
|
||||
np.testing.assert_allclose(f.quantile(0.5), 0.0, atol=0.02)
|
||||
assert f.dates[0] == pd.Timestamp("2020-01-11")
|
||||
@@ -0,0 +1,83 @@
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
from btcmodel import data
|
||||
from btcmodel.evaluate import backtest
|
||||
from btcmodel.halving import HALVINGS, cycle_position
|
||||
from btcmodel.models import MODELS, CycleModel
|
||||
|
||||
|
||||
def synthetic_prices(daily_return, start="2011-01-01", end="2024-11-26", noise=0.0, seed=0):
|
||||
"""Prices whose daily log return is `daily_return(cycle_day)` plus optional noise."""
|
||||
dates = pd.date_range(start, end, freq="D", name="date")
|
||||
_, day = cycle_position(dates)
|
||||
r = daily_return(day) + noise * np.random.default_rng(seed).standard_normal(len(dates))
|
||||
return pd.DataFrame({"close": 100 * np.exp(np.cumsum(r))}, index=dates)
|
||||
|
||||
|
||||
def test_cycle_position():
|
||||
index, day = cycle_position(pd.DatetimeIndex(["2009-01-03", "2012-11-27", *HALVINGS]))
|
||||
assert list(index) == [0, 0, 1, 2, 3, 4]
|
||||
assert list(day) == [0, 1424, 0, 0, 0, 0]
|
||||
# A projected halving about four years after the last one starts cycle 5.
|
||||
index, _ = cycle_position(pd.DatetimeIndex(["2028-06-01"]))
|
||||
assert index[0] == 5
|
||||
|
||||
|
||||
def test_cycle_model_recovers_a_cycle_shaped_drift():
|
||||
# Up for the first half of each cycle, down in the second half.
|
||||
def shape(day):
|
||||
return np.where(day < 700, 0.002, -0.001)
|
||||
|
||||
prices = synthetic_prices(shape)
|
||||
drift = CycleModel(prior_days=0).drift_by_cycle_day(prices)
|
||||
assert drift[300] == pytest.approx(0.002, abs=2e-4)
|
||||
assert drift[1100] == pytest.approx(-0.001, abs=2e-4)
|
||||
|
||||
|
||||
def test_cycle_model_weights_recent_cycles_more():
|
||||
# The same day of the cycle returns less in each later cycle.
|
||||
prices = synthetic_prices(lambda day: np.zeros_like(day, dtype=float))
|
||||
cycle, _ = cycle_position(prices.index)
|
||||
r = 0.004 / 2.0**cycle
|
||||
prices["close"] = 100 * np.exp(np.cumsum(r))
|
||||
drift = CycleModel(recency_half_life=0.25).drift_by_cycle_day(prices)
|
||||
equal = CycleModel(recency_half_life=1e9).drift_by_cycle_day(prices)
|
||||
latest_complete = 0.004 / 2.0**3
|
||||
assert abs(drift[900] - latest_complete) < abs(equal[900] - latest_complete)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("name", list(MODELS))
|
||||
def test_models_produce_valid_forecasts(name):
|
||||
prices = synthetic_prices(lambda day: 0.001 + 0 * day, noise=0.03)
|
||||
horizons = np.array([1, 30, 365, 1460])
|
||||
f = MODELS[name].forecast(prices, horizons)
|
||||
assert f.log_quantiles.shape == (4, 100)
|
||||
assert np.all(np.diff(f.log_quantiles, axis=1) >= 0)
|
||||
lo, hi = f.interval(0.8)
|
||||
assert np.all(np.diff(hi - lo) > 0), "uncertainty should grow with horizon"
|
||||
|
||||
|
||||
def test_backtest_never_shows_models_the_future():
|
||||
prices = synthetic_prices(lambda day: 0.001 + 0 * day, noise=0.03)
|
||||
|
||||
class Spy:
|
||||
name = "random_walk"
|
||||
|
||||
def forecast(self, history, horizons):
|
||||
origin = history.index[-1]
|
||||
assert history.index.max() == origin
|
||||
assert (origin + pd.Timedelta(days=int(horizons.min()))) > history.index.max()
|
||||
return MODELS["random_walk"].forecast(history, horizons)
|
||||
|
||||
scores = backtest([Spy()], prices)
|
||||
assert len(scores) > 0
|
||||
targets = scores.origin + pd.to_timedelta(scores.horizon, unit="D")
|
||||
assert targets.max() <= prices.index[-1]
|
||||
|
||||
|
||||
def test_development_data_stops_at_cutoff():
|
||||
prices = data.load_prices(until=data.DEV_CUTOFF)
|
||||
assert prices.index[0] == data.DATA_START
|
||||
assert prices.index[-1] == data.DEV_CUTOFF
|
||||
Reference in New Issue
Block a user