Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
5,018 documents
The article presents HyperGPT as a blockchain-based marketplace for exchanging and monetizing AI services. It describes a model in which a native token supports payments, access to premium features, and community rewards. Partnerships are used to illustrate…
The document surveys privacy and safety measures across consumer technology. For smartphones, it describes a hardware-based display feature intended to limit viewing by bystanders. In blockchain, it introduces protocol-level privacy and fully homomorphic…
The article introduces Turbo as an Ethereum-based meme token whose creation involved GPT-4 and community polling. It describes an AI-assisted process for developing the token’s concept and branding, and reports that its total and circulating supply are both…
The white paper describes BIO as a utility token for the BIO Protocol, a decentralized science platform. It states that the token is issued on Ethereum, Base, and Solana, with a maximum supply of 3.32 billion. Listed uses include proposing and voting on…
The article attributes KAITO’s June 2025 rise to interest in Kaito’s AI-based crypto information platform, user incentives, partnerships, and investor backing. It describes Kaito Pro as aggregating information from many sources and the Yaps Program as…
The article describes how interest-rate expectations, inflation concerns, regulation, and institutional activity may shape Bitcoin and Ethereum ETF flows in 2025. It distinguishes short-term withdrawals, which it links to macro uncertainty, profit-taking,…
The document outlines a qualitative framework for assessing early-stage crypto projects. It highlights technology addressing a clear problem, community engagement, alignment with market narratives, and token economics such as utility, staking, and supply…
The article surveys business models and activity it associates with cryptocurrency profitability, including Bitcoin mining, AI compute services, cloud mining, DeFi fees, Ethereum layer-2 scaling, Solana applications, token buybacks, DePIN, and tokenized…
The article presents Ethereum as a possible coordination and settlement layer for AI agents. It argues that public verification, neutrality, and smart contracts could support agent-to-agent transactions and other automated financial flows. It also describes…
The document introduces TAGGER as a blockchain and Web3 platform for authenticating, labeling, managing, and trading data used in AI. It describes a decentralized marketplace intended to reduce data silos and enable permissionless exchange, while rewarding…
This long-only system combines a volume-weighted moving average with a smoothed RSI variant. It seeks entries when the close is above the VWMA and the smoothed RSI is above its threshold; exits occur when price falls below the average and RSI drops below its…
This workflow describes automated cryptocurrency perpetual-futures trading that delegates per-asset decisions to a large language model. An hourly process gathers market indicators, funding rates, positions, account state, and historical trading performance,…
This machine-learning example predicts whether each crypto futures asset’s next closing price will rise or not, then uses those binary predictions as portfolio weights. It builds candidate features from futures data, including a weighted moving-average trend…
This tutorial template uses a Ridge Classifier to predict whether a stock's next closing price will be higher or lower than its current close. Its main example loads Amazon data from the Nasdaq-100 universe, uses log closing prices as features, and reserves…
This proposed strategy identifies direction from a short and a long simple moving average, with crossovers used to open positions. RSI is presented as a stand-in for machine-learning confidence: the code scales and rounds the indicator, then applies a…
This example demonstrates using a persistent object store to cache historical data so later backtests can avoid repeating a costly history request. On the first run, it retrieves daily SPY closing prices, feeds them into price and exponential moving average…
This EUR/USD strategy outline combines linear regressions over short, medium, and long lookback windows. The visible code calculates each regression’s current fitted value, slope, R-squared, correlation, and projected value at a future horizon. Inputs…
This document describes an automated cryptocurrency trading system organized around two workflows: an hourly analysis and decision cycle, and a separate frequent monitor for take-profit and stop-loss conditions. The analysis cycle gathers multi-timeframe…
This strategy combines a fixed neural network with price and technical indicator inputs, RSI-based adaptive stops, and a Super Trend filter. The network processes changes in volume, Bollinger Band measures, RSI, and MACD histogram to produce an output used…
This script trains a random forest classifier to label short-term Bitcoin price changes as rising, falling, or relatively flat. It samples the latest price hourly, calculates consecutive percentage changes over a rolling window, and assigns directional…
This daily GLD strategy applies a fixed linear score to five features derived from price and volume, including short moving averages and interaction terms. The feature weights and normalization constants are embedded in the script, and the strategy holds a…
This automated strategy uses the Kronos financial time-series model to forecast a future price distribution. It compares the forecast median with the current price to decide whether the projected opportunity is large enough to trade, while the lower and…
This document outlines an automated system that uses a large language model to produce entry, hold, or close decisions for multiple cryptocurrency perpetual futures. Its pipeline collects market and account data, calculates indicators across short and long…
This algorithm trains a separate feedforward neural network for each of three exchange-traded funds using recent daily opening prices. The model uses one input layer with a hidden ReLU layer and a single output, and is trained to predict the next opening…