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.
1,747 documents
The document explains why candle-based backtests can mislead, especially for high-frequency and multi-instrument strategies. Candles omit the timing of trades and bid-ask quotes, making it hard to reconstruct simultaneous prices or realistic fills. They also…
The article presents a simple long-only strategy for crypto perpetual contracts. Starting from the current price, it places buy orders at fixed intervals below that level, increasing order size by a configured ratio as price falls. When a long position…
This document outlines a utility for collecting and monitoring perpetual futures funding rates across several exchanges. It assigns a worker thread to each platform, applies platform-specific request delays, and uses different retrieval methods depending on…
This document describes a monitoring tool that collects perpetual futures funding rates from several exchanges, stores results separately for each platform, and displays them in a shared table. Exchange-specific polling intervals and market lookup methods…
This tutorial presents a beginner framework for testing factors across a universe of USDT perpetual futures. It starts with hourly price, volume, trade count, and aggressive-buy data, then describes factor evaluation through forward-return regressions,…
The article studies how cryptocurrency prices behaved after Binance began trading their perpetual contracts in 2023. It aligns four-hour closing-price data to each contract's first available observation, excludes contracts already listed at the start of the…
The article introduces intertemporal hedging by pairing a long position in one contract with a short position in another, then closing both when the combined result is favorable. It derives the basic spread relationship for a short in contract A and a long…
This analysis asks whether Binance-listed perpetual contracts keep rising after launch or tend to reverse. It uses four-hour Binance perpetual price data from 2023, measuring each newly listed coin’s prices against its first available price and averaging the…
This article recounts adapting an older FMZ market making strategy for Lighter, a decentralized exchange described as offering zero trading fees to regular traders. It explains the exchange account configuration at a high level and argues that lower fees…
The article outlines a cryptocurrency high-frequency approach that combines short-term trend detection with two-sided quoting. It first gauges direction from recent trade flow: average trade size and frequency, spread changes, and buy and sell execution…
The article reviews changes in cryptocurrency perpetual-futures funding-rate arbitrage. It says that as more capital entered the strategy, positive premiums and negative-premium exit opportunities became smaller, while slippage rose and attractive rates…
The article describes a rebalancing approach for perpetual futures: maintain a target position value, sell as price rises, and buy as it falls. An adjustment threshold determines how much price movement triggers a rebalance. It contrasts this with a grid…
This strategy shorts high-gaining Binance USDT perpetual contracts, screening for coins that may be near a reversal. It gathers open interest, funding, market capitalization, and daily candle data, then applies hard exclusions and an AI model’s weighted…
The document walks through a JavaScript implementation of a two-asset digital currency pair strategy on a trading platform. It estimates an average price ratio from aligned hourly candles, compares the current ratio with that average, and converts the…
This article presents an FMZ framework for trading several perpetual futures symbols through unified platform APIs. It organizes per-symbol state for prices, orders, positions, precision, and account metrics, then describes routines for loading market…
This article walks through connecting Claude Pro to an FMZ account through the Model Context Protocol (MCP), then using natural-language requests to inspect and operate trading tools. Demonstrations include checking running strategies, placing a series of…
This article describes a monitoring script that polls funding rates for perpetual futures across multiple exchanges. It uses a separate worker for each exchange, applies exchange-specific delays to reduce request pressure, and handles venues differently…
This article describes an automated crypto futures approach that treats unusually large liquidation flows as potential trend continuation signals. It establishes a per-asset baseline by dividing historical liquidation amounts into time windows, then uses a…
This document describes an experiment integrating Jev’s structured judgments into a Binance USDT-margined perpetual strategy. The model receives order-book state, recent trades, short-term price changes, and volatility, while positions, order sizes, and…
The document describes APFF, a cross-sectional long-short strategy for USDT-margined perpetual contracts. It ranks a universe of contracts using five seed factor families: momentum, short-term reversal, funding rates, premium, and open interest. Raw signals…
The article presents an automated strategy for Binance perpetual contracts after a delisting signal. It detects contracts whose delivery date changes from a distant placeholder to a scheduled settlement time, then opens a base short and runs a separate short…
This article describes a crypto perpetual-futures strategy that combines short-term trend detection with passive market making. It uses aggregated trades to estimate the size and frequency of buyer- and seller-initiated activity, while order-book depth and…
The article walks through an automated crypto perpetual-futures system that gathers account, market, and position data, sends a structured context to a large language model, and routes its output to an execution module. Its market inputs combine…
The article presents an automated workflow for discovering and refreshing cross-sectional factors for crypto perpetual contracts. It selects liquid markets, classifies volatility conditions, prompts an AI system to propose factors, and evaluates candidates…