Strategy Academy
Understand the edge behind the signals
Our 3-factor long-only execution bot that buys the top-conviction setups the moment they confirm, and exits on the first sign of fade.
Read more →The short-only counterpart to MB. Uses the same 3-factor score in reverse - fading the tokens that ran farthest before the crowd rotates out.
Read more →Momentum-continuation short. Targets the most beaten-down tokens (XS rank bottom 20%) that are still actively falling - confirmed by a declining 1h rank and a negative 1h XS delta. Holds up to 4 hours; exits when the trend reverses.
Read more →Mean-reversion long. Buys the most beaten-down tokens (XS bottom 25%) in freefall (TS 5–35), expecting a sharp bounce. A consecutive loss gate blocks chronic non-bouncers. Valid in all market regimes - 92.8% win rate in backtest.
Read more →Runs long and short simultaneously from a single account - capturing momentum in both directions and naturally offsetting drawdown when the market is indecisive.
Read more →How we rank ~600 Bybit perpetuals by relative momentum score and select the top-performing setups.
Read more →Position sizing, stop-loss architecture, daily loss limits, and the leverage ladder used on live positions.
Read more →How BTC price action and market breadth define the regime filter that gates all signal entries.
Read more →End-to-end flow from data collection through signal scoring, filtering, and delivery to your API endpoint.
Read more →Momentum investing is one of the most robustly documented anomalies in financial markets. First described by Jegadeesh and Titman (1993), the strategy of buying recent winners and selling recent losers has been validated across equities, commodities, currencies, and-most recently-crypto assets.
MasterBitcoin’s signals are generated using a cross-sectional momentum (XS-MOM) framework adapted for the unique characteristics of crypto perpetuals: high volatility, 24/7 trading, funding rate dynamics, and regime sensitivity.
Key Concepts
Core terminology used throughout the platform
Built on Published Research
Every gate and filter traces back to peer-reviewed literature
The foundational paper documenting the momentum anomaly: stocks with high returns over the past 3–12 months continue to outperform over the next 3–12 months. Replicated across nearly every asset class studied since.
Documents the optionality-like payoff of momentum: large positive returns in trending markets, but severe crashes during volatility spikes and bear-market reversals. The theoretical basis for our regime gating system.
Validates a long-only cross-sectional momentum edge specifically in cryptocurrency markets. Confirms the anomaly persists after accounting for funding rates and the structural short-selling frictions in crypto perpetuals.
Shows that scaling portfolio exposure inversely with recent realised volatility improves Sharpe ratio for momentum strategies. The academic foundation for the volatility-targeting position-sizing applied to top strategies.
Establishes a three-factor model - market, size, and momentum - that explains cross-sectional returns across 1,800+ cryptocurrencies from 2014–2020. Confirms momentum as a persistent return predictor and provides the factor framework behind multi-signal scoring approaches.
Analyses funding rate mechanics across crypto perpetual futures markets. Shows that positive funding rates create persistent carry costs for short positions - a structural headwind that systematically disadvantages short-selling strategies in trending markets and reinforces the long-only approach.
Finds that unlike equity markets, crypto anomaly returns are concentrated in the long leg. Limits to arbitrage - margin frictions, regulatory barriers on institutional short-selling, and funding costs - prevent the short side from being exploited, making long-only the structurally sound choice.
Documents that symmetric long-short momentum in crypto suffers severe, regime-dependent crashes. Volatility-managed and long-biased approaches significantly outperform the standard long-short construction - validating regime gating and volatility scaling as essential risk controls.
Performance Principles
- Calmar ratio as the primary performance metric
- Signal quality per market regime
- Drawdown control over raw return maximisation
- Win rate segmented by 1h, 4h, and 24h timeframe
- Alpha decay over rolling lookback windows
- MFE and MAE distributions per signal tier
- Overfitting parameters to historical data
- Data snooping across in-sample backtests
- Leverage deployment without validated edge