Algorithmic Trading

When financial AI meets the optimizer arms race, the stakes are measured in both milliseconds and market moves. The recent From Rattle to Roar study tests this premise with MambaStock — a selective state-space model — trained to forecast S&P 500 weekly returns. The twist: pitting eight widely-used optimizers against a new family called Roaree, designed to capture Lion’s speed while taming its instability. Why Optimizers Matter More in Finance Than You Think In financial forecasting, milliseconds can mean the difference between execution and regret. This makes optimizer choice not just a theoretical concern but a practical lever for profitability. The study reinforces that adaptive-rate, momentum-based methods (Adam, RMSProp, Nesterov) deliver the lowest test errors for noisy, small-magnitude financial returns. Vanilla SGD struggles in this regime; AdamW’s decoupled weight decay over-regularizes, slowing convergence in already weak-signal environments. ...

In a field where LSTMs, transformers, and black-box agents often dominate the conversation, a new framework dares to ask: What if our old tools weren’t wrong, just under-optimized? That’s the central premise behind Technical Indicator Networks (TINs) — a novel architecture that transforms traditional technical analysis indicators into interpretable, trainable neural networks. Indicators, Meet Neural Networks Rather than discarding hand-crafted indicators like MACD or RSI, the TIN approach recasts them as neural network topologies. A Moving Average becomes a linear layer. MACD? A cascade of two EMAs with a subtractive node and a smoothing layer. RSI? A bias-regularized division circuit. The resulting neural networks aren’t generic function approximators; they’re directly derived from the mathematical structure of the indicators themselves. ...

When it comes to trading the S&P 500, tradition says: trust the chart. But a new study from UCLA researchers proposes a smarter compass—one that listens not only to price momentum but also to the tone of the news. By merging language model-powered sentiment scores with technical indicators and time-series forecasting, the authors build a hybrid strategy that outperforms a buy-and-hold baseline during a volatile 3-month window. ...

In the noisy world of algorithmic trading, volatility is often treated as something to manage or hedge against. But what if it could be a signal generator? Ivan Letteri’s recent paper proposes a novel trading framework that does just that: it treats mid-range volatility not as a nuisance, but as the key to unlocking directional causality between assets. From Volatility to Causality: The 4-Step Pipeline This is not your standard volatility arbitrage. The author introduces a four-stage pipeline that transforms volatility clusters into trading signals: ...