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.
5,922 documents
The article describes The Smarter Web Company’s decision to allocate part of its corporate reserves to Bitcoin alongside its digital services business. It frames the allocation as a long-term treasury policy intended to preserve retained earnings against…
The article compares Bitcoin and Ether through their consensus systems, supply characteristics, and roles in digital assets. Bitcoin uses proof of work and has a capped supply, which the text connects to its store-of-value narrative. Ethereum moved to proof…
The document surveys why companies may hold Bitcoin in treasury, including diversification, perceived scarcity, and an intended hedge against fiat currency depreciation. It describes possible approaches to managing the position: assess risk tolerance, plan…
The document compares DePIN token performance with the broader crypto market and describes differences among bandwidth, storage, and connectivity projects. It reports sector-level declines alongside stronger relative performance, and highlights AIOZ’s large…
The article uses DMG Blockchain as an example of a corporate treasury considering digital assets beyond Bitcoin. It frames diversification as a way to manage liquidity and volatility while retaining Bitcoin as a core holding. It also discusses possible…
The document explains why companies may hold cryptocurrencies as balance-sheet reserves, describing goals such as diversifying capital, responding to adoption trends, and seeking long-term shareholder value. It contrasts Bitcoin-focused treasury plans with a…
This beginner guide proposes evaluating crypto assets by market capitalization and liquidity, use case, security history, development activity, and community. It recommends building a core around Bitcoin and Ethereum, with Solana as a higher-growth…
The document describes Yield Basis, a Bitcoin-focused DeFi protocol built around Curve’s automated market maker design. It says the protocol uses automated re-leveraging to keep liquidity-provider exposure closer to underlying BTC and reduce impermanent…
This framework example demonstrates combining multiple risk controls in one portfolio risk-management model. It applies a maximum unrealized profit percentage rule and a maximum drawdown percentage rule to each security, showing how separate controls can be…
This example applies technical signals calculated from the S&P 500 index to a universe of Nasdaq 100 stocks. It constructs separate strategies from the index's TRIX, RSI, or rate of change, then assigns liquid stocks binary exposure when paired comparisons…
This example demonstrates a basic stock-universe selection process. At the coarse-selection stage, it ranks available securities by daily dollar volume and retains the three highest-ranked symbols. The algorithm requests daily data and starts with a…
This market-neutral strategy ranks USDT perpetual contracts using a weighted composite of cross-sectional price momentum and funding-rate information. It goes long the highest-scoring coins and short the lowest-scoring ones. Funding intervals are normalized…
The document outlines a composite, low-frequency CTA approach that combines multiple factors, markets, time horizons, and strategy types. Its components include trend following, mean-reversion signals intended to offset trend exposure, swing trading that…
This UMD strategy ranks stocks by returns over a long lookback while omitting the most recent month, then buys the strongest group and shorts the weakest. It updates the selections monthly, uses equal weighting, and delays positions by one period before…
This document describes a bot that maintains target portfolio weights by comparing each asset’s current value with its assigned share of total portfolio value. For each asset, configurable buy and sell deviation thresholds determine when the bot trades…
This alpha model forms every eligible pair from the selected securities and tracks the ratio of the first asset’s price to the second’s. It smooths that ratio with an exponential moving average and sets upper and lower bands using a configurable percentage…
This strategy ranks configured equity sectors by the average lookback return of their valid constituents, using closed four-hour bars. It buys every member of the strongest sector and shorts every member of the weakest, with equal total notional on each…
The code describes three candidate factors for ranking crypto assets: an ATR-based volatility measure, a volume-distribution measure, and an inverse-price measure. The volatility function calculates ATR over 14 periods, ranks the observations, and maps the…
This example shows how to include technical signals in coarse fundamental universe selection. It maintains fast and slow exponential moving averages for each symbol, updated with adjusted daily prices, and retains securities whose fast average exceeds the…
The document describes a sector rotation approach combining relative momentum with a market regime filter. Its stated rules hold the strongest sectors by three-month gains when the S&P 500 is above its ten-month simple moving average, exit when the index…
The document outlines a rule-based strategy for Hong Kong’s Hang Seng leveraged ETF 00631L. It starts with half of the available capital invested and compares unrealized profit with remaining cash. When either side exceeds the other by the stated margin, the…
This strategy forms a spread from two instruments using hedge weights estimated with the Johansen cointegration procedure on a rolling lookback. It recalculates those weights daily, then measures the spread against its rolling mean and standard deviation. A…
This Binance spot strategy rebalances a selected basket of crypto assets toward configured target weights. It values each holding using the midpoint of the bid and ask, compares each asset’s share of the tracked account value with its target allocation, and…
This strategy keeps an asset at a chosen share of portfolio equity. It opens a position using the target percentage and initial capital, then compares the position’s current value with account equity. If the share falls below target, it buys; if it rises…