The In-Memory C++ Leap in Blockchain Analysis - Caudena

Real-Time, Court-Admissible Crypto Intelligence at 1/400th the Cost of Inferior Legacy Systems The explosion of blockchain data isn't just a challenge; it's a crisis for conventional analytics. Financial institutions, investigators, and law enforcement agencies are hamstrung by tools that are too slow, expensive, and built on legacy database technologies incapable of keeping pace. Critical insights

Maximizing Battery Storage Profits via High-Frequency Intraday Trading

Maximizing revenue for grid-scale battery energy storage systems in continuous intraday electricity markets requires strategies that are able to seize trading opportunities as soon as new information arrives. This paper introduces and evaluates an automated high-frequency trading strategy for battery energy storage systems trading on the intraday market for power while explicitly considering the dynamics of the limit order book, market rules, and technical parameters. The standard rolling intrinsic strategy is adapted for continuous intraday electricity markets and solved using a dynamic programming approximation that is two to three orders of magnitude faster than an exact mixed-integer linear programming solution. A detailed backtest over a full year of German order book data demonstrates that the proposed dynamic programming formulation does not reduce trading profits and enables the policy to react to every relevant order book update, enabling realistic rapid backtesting. Our results show the significant revenue potential of high-frequency trading: our policy earns 58% more than when re-optimizing only once every hour and 14% more than when re-optimizing once per minute, highlighting that profits critically depend on trading speed. Furthermore, we leverage the speed of our algorithm to train a parametric extension of the rolling intrinsic, increasing yearly revenue by 8.4% out of sample.

TradExpert: Revolutionizing Trading with Mixture of Expert LLMs

The integration of Artificial Intelligence (AI) in the financial domain has opened new avenues for quantitative trading, particularly through the use of Large Language Models (LLMs). However, the challenge of effectively synthesizing insights from diverse data sources and integrating both structured and unstructured data persists. This paper presents TradeExpert, a novel framework that employs a mix of experts (MoE) approach, using four specialized LLMs, each analyzing distinct sources of financial data, including news articles, market data, alpha factors, and fundamental data. The insights of these expert LLMs are further synthesized by a General Expert LLM to make a final prediction or decision. With specific prompts, TradeExpert can be switched between the prediction mode and the ranking mode for stock movement prediction and quantitative stock trading, respectively. In addition to existing benchmarks, we also release a large-scale financial dataset to comprehensively evaluate TradeExpert's effectiveness. Our experimental results demonstrate TradeExpert's superior performance across all trading scenarios.

Trading with Claude (and writing your own MCP server)

Burrito Now, Pay Later

Liquid Lunches And The Serious Case for BNPL Securitization

Stripe’s payments APIs: The first 10 years

Abstracting away the complexity of payments has driven the evolution of our APIs over the last decade. Learn more about Stripe payments APIs here.

Fintech founder charged with fraud after 'AI' shopping app found to be powered by humans in the Philippines | TechCrunch

Albert Saniger, the founder and former CEO of Nate, an AI shopping app that promised a “universal” checkout experience, was charged with defrauding

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