Research Articles

How to find profitable market entry regimes across multiple strategies and turn them into reusable allowlists.

RLXBT

⏱ 3m 👁 381 ♥ 1

In this article, we explore how using strict Exit Rules affects the training and performance of Reinforcement Learning (RL) agents in the cryptocurrency market.

RLXBT

⏱ 9m 👁 318 ♥ 0

Filtering pullbacks using market efficiency (fractal dimension) and institutional limit order absorption turns a loss-making pullback ruleset into a robust, high-Sharpe trading strategy on BTCUSDT 1h.

Serg

⏱ 2m 👁 133 ♥ 0

The definitive multivariable combo strategy on BTCUSDT 1h, out-of-sample validated under realistic commissions.

Serg

⏱ 1m 👁 123 ♥ 0

An in-depth quantitative analysis of the Custom Squeeze-Release Short strategy on BTCUSDT 1h, exploring volatility compression, Shannon entropy, return z-scores, and walk-forward parameter decay.

Serg

⏱ 4m 👁 116 ♥ 0

Robust mean-reversion strategy entering longs on oversold conditions (RSI_14 < 30 and close < BB_Lower) and exiting on recovery. Proven highly robust with WFE = 2.01.

Serg

⏱ 2m 👁 113 ♥ 0

A guide on using the RLXBT Deep Reinforcement Learning (DQN) engine and MCP integrations to train, evaluate, and predict market signals dynamically using AI agents.

Serg

⏱ 3m 👁 111 ♥ 0

A guide on using the RLXBT Deep Reinforcement Learning (DQN) engine and MCP integrations to train, evaluate, and predict market signals dynamically using AI agents.

Serg

⏱ 3m 👁 111 ♥ 0

A textbook Bollinger-band fade looks tradeable in-sample - positive Sharpe, ~50% win rate over 1,291 trades. Walk-forward analysis kills it. A short case study in why out-of-sample validation is non-negotiable.

Serg

⏱ 2m 👁 110 ♥ 0

Symmetrical dual squeeze-release breakouts combined with Shannon price entropy exits cure the out-of-sample parameter decay seen in directional breakout strategies on BTCUSDT 1h.

Serg

⏱ 2m 👁 109 ♥ 0

A guide on using the RLXBT Deep Reinforcement Learning (DQN) engine and MCP integrations to train, evaluate, and predict market signals dynamically using AI agents.

Serg

⏱ 3m 👁 105 ♥ 0

Short-only strategy based on custom squeeze-release indicators on BTCUSDT. Backtest shows Sharpe 1.64 and low drawdown, but WFE of 0.35 signals high potential for overfitting.

Serg

⏱ 2m 👁 98 ♥ 0

A 16-altcoin BTC-led portfolio achieved gross Sharpe 10.73—but turnover costs were still 26.6 times larger than its alpha.

Serg

⏱ 6m 👁 95 ♥ 0

Weekly crypto momentum survived fees and funding, gained 106% in 2025, then reversed to -43% in the untouched 2026 test. Volatility scaling did not save it.

Serg

⏱ 6m 👁 91 ♥ 0

A guide on using the RLXBT Deep Reinforcement Learning (DQN) engine and MCP integrations to train, evaluate, and predict market signals dynamically using AI agents.

Serg

⏱ 3m 👁 73 ♥ 0

A robust volatility breakout strategy on BTCUSDT 1h, gated by Hurst Exponent and Fractal Dimension Index, out-of-sample validated under real fees.

Serg

⏱ 1m 👁 71 ♥ 0

Why a 'modest' 1.5% unleveraged return over 9 months is actually a major quant victory. We break down capital efficiency (1.67% time-in-market), leverage scaling, and overcoming transaction friction.

Serg

⏱ 3m 👁 68 ♥ 0

A native RLXBT agent searched for a BTC 1h Morning Star, froze the pattern before evaluation, beat a directional baseline—and still refused to call three trades a validated strategy.

Serg

⏱ 4m 👁 61 ♥ 0