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.
52 documents
The article explains how leveraged perpetual futures positions can be liquidated when traders fail to meet maintenance margin requirements. It treats liquidation data as forced buy or sell order flow that may reveal short-term market pressure, and describes…
This article outlines factors to assess before depositing token pairs into a decentralized exchange liquidity pool. Liquidity providers receive a share of swap fees, generally represented by redeemable pool tokens, and some pools may also distribute…
The article surveys possible uses of artificial intelligence in crypto trading and decentralized finance. It discusses robo-advisory, automated bots, strategy development and backtesting, risk assessment, arbitrage monitoring, sentiment analysis, predictive…
The document explains how crypto data aggregators combine information from centralized and decentralized exchanges into normalized time series. It frames fragmentation across venues, trading pairs, and blockchains as an infrastructure problem for…
The article explains Active Fundamental Performance (AFP), a measure intended to identify mutual fund managers who select stocks well on fundamental information. For each fund, it computes the covariance between benchmark-adjusted portfolio weights, or…
This guide explains how to run a Python trading strategy backtest with LumiBot, choose a historical data provider, configure dates and sources, and review generated output. It describes ThetaData, Yahoo Finance, Polygon, custom Pandas data, and Polymarket…
The article outlines how Amberdata datasets can be accessed through Snowflake, Google Analytics Hub, and Databricks. It frames these integrations as a way for institutional researchers and traders to work with historical and fresh digital-asset data using…
The article describes how historical crypto options data can support market research, algorithm development, and portfolio management. It identifies implied volatility, realized volatility, open interest, put/call ratios, price and volume records, and order…
The guide explains how impermanent loss arises when the relative prices of two tokens change while they are held in an automated market maker (AMM) liquidity pool. It contrasts a liquidity position with simply holding the deposited assets, describes how…
This report explains how to design a cryptocurrency arbitrage strategy across centralized exchanges and decentralized exchanges using automated market maker pools. It covers the differences between order-book prices and pool pricing, then identifies costs…
This introduction to crypto pairs trading argues that correlation alone does not establish a durable relationship between two assets. A pair may move together because of shared market forces, yet its price spread can continue drifting. Cointegration offers a…
This guide outlines a crypto pairs mean-reversion strategy built around cointegration rather than correlation alone. It proposes testing logged price series with the Engle–Granger method, estimating a regression hedge ratio, and checking the resulting spread…
The document explains how implied volatility (IV) surfaces organized by option moneyness can help identify relative pricing anomalies. A floating surface compares options at their actual listed expirations, while a constant surface interpolates or…
This article applies the stock-to-flow ratio to Bitcoin as a scarcity-based valuation approach. It defines stock as the existing supply and flow as new annual issuance, then estimates Bitcoin’s annual production from the change in supply over a year. Using…
This note describes a two-stage ranking factor for equities. For each stock, it ranks the recent ten-day low-price observations through time, then ranks those values across stocks on the same date. The intended interpretation is that a larger final factor…
The document introduces Uniswap V3’s concentrated liquidity, where providers allocate liquidity within chosen price ranges, and explains how to examine decentralized exchange activity through trade records, prices, and OHLCV aggregates. Individual trades…
This post outlines a Chinese equity screen that combines an amplitude threshold, a proxy for institutional buying, and a recent large daily gain. The intended logic is to find volatile stocks attracting institutional interest that have also shown a strong…
The document argues that informed DeFi analysis requires four complementary data views: protocol, pool, asset, and wallet. Protocol data supports comparisons across financial functions such as lending, staking, and asset management. Pool data describes…
The report describes tests of 14 Ethereum trading strategies using stablecoin issuance and Uniswap V2 USDC/ETH pool activity. It outlines signals based on rolling issuance sums, moving averages, standard-deviation thresholds, Pearson correlation, and pool…
This case study describes a hedge fund seeking to add digital asset strategies and the data infrastructure needed to research and trade them. Its requirements included real-time and historical market data, high-volume feeds for algorithm development and…
The post presents a Chinese equity screen combining daily price range, a ranking based on net large-order activity, and a minimum market-capitalization condition. Its stated rationale is to find stocks with notable volatility and trading activity while…
The article describes how institutional crypto data services can support research, trading, and risk management. It highlights combining real-time and historical information from centralized exchanges, decentralized venues, and blockchains, including spot…
The article surveys how market and blockchain data may inform long-term crypto investing and short-term trading. For fundamental research, it lists measures such as market capitalization, supply, trading activity, network use, token holders, velocity, total…
The document outlines a basic backtesting workflow: define a strategy, gather historical data, calculate returns and supporting statistics, then decide whether to deploy or refine the idea. It notes that crypto strategies may use pairs trades, rebalancing…