Selected work
05 · Time-series machine learning
Bitcoin Direction Model
An exploratory three-day Bitcoin-direction study that prioritizes chronological validation and uncertainty over headline accuracy.
observations4,062
features38
test accuracy53.93%
chronological split · TimeSeriesSplit · bootstrap interval
Executive summary
A quantitative research workflow built from market and on-chain data to test whether engineered signals improve directional classification on unseen data.
Problem
Financial time-series prediction is prone to look-ahead bias. The project tests several algorithms while preserving chronological splits and making performance uncertainty visible.
Approach
- 01Merge daily price history and on-chain MVRV data, then construct momentum, trend, volatility, oscillator, lag, and valuation features.
- 02Use a three-day forward-return target and remove near-flat movements under a stated ±1% filter.
- 03Train linear, logistic, Lasso, random-forest, and XGBoost models using chronological splits and TimeSeriesSplit validation.
- 04Use bootstrap confidence intervals to contextualize directional test accuracy rather than treating one score as conclusive.
02
Data & methodology
Data sources
- Yahoo Finance daily BTC data
- CoinMetrics Community API MVRV series
Methods
- 4,062 usable daily observations before target filtering
- 38 engineered market and on-chain features
- Chronological 80/20 split with TimeSeriesSplit
- 1,000-resample bootstrap confidence intervals
Workflow
Input→Collection→Analysis→Output
Results are reported as documented in the project report and are presented with their study constraints.
Outputs & findings
Key outputs
- Executive research report
- Model comparison
- Feature-engineering specification
- Validation and confidence-interval analysis
What the work demonstrates
- 3,178 observations remained after the project’s stated near-flat-return filter.
- XGBoost reached 53.93% directional test accuracy; its reported 95% bootstrap interval was [50.31%, 57.70%].
Limitations
- Exploratory research, not a live trading strategy or investment recommendation.
- Directional accuracy alone does not establish tradability, robustness across regimes, or net performance after costs.
- The filter and data period materially shape the evaluation set.