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
This tutorial outlines a statistical arbitrage approach for crypto perpetual contracts. It screens historical closing prices for highly correlated pairs, then trades deviations in their price ratio: when the ratio is above its reference level, it buys one…
This strategy screens USDT perpetual contracts for strong 24-hour gainers, then collects open interest, funding rates, market capitalization, and daily candles. Hard filters remove crowded shorts, low open interest, and coins that have already pulled back.…
The document reconstructs a TradingView strategy that combines a fast and slow exponential moving average with buy and sell signals from a range filter indicator. A long entry requires a bullish range-filter signal, the fast EMA above the slow EMA, and a…
The article describes integrating Jev, a model that returns choices, scores, or probabilities, into a Binance USDT-margined perpetual strategy. It feeds the model order-book data, recent trades, short-term price changes, and volatility, while local code…
This overview introduces grid trading as a way to trade price fluctuations without forecasting a single market direction. It explains the basic approach of placing buy and sell orders at price levels across a range, then compares this with rebalancing, which…
This guide describes how to connect to dYdX v4 and use its indexer and chain interfaces for market data, account and position queries, orders, transfers, and transaction lookups. It distinguishes the indexer, which serves REST and WebSocket data, from…
This document describes a monitoring system for price differences between decentralized and centralized exchanges. It groups configured venues by exchange type, normalizes symbol names that vary across platforms, loads market precision, and requests order…
This article explains a one-times-leveraged short position in a coin-margined perpetual contract as a way to seek funding payments while keeping the position’s dollar value relatively stable. It describes how fixed-value contracts change the amount of coin…
The article proposes a market-neutral strategy for volatile crypto perpetual futures. It ranks contracts using price momentum and funding rates, then holds equal-notional long and short baskets so performance depends on relative strength rather than…
The article develops a market-neutral rotation strategy for volatile perpetual futures. It ranks contracts using a composite of price momentum and funding rates, taking long positions in the strongest names and shorts in the weakest with balanced notional…
The article surveys four cryptocurrency strategies. A funding rate trade pairs a short perpetual futures position with a long spot holding to seek positive funding while hedging directional exposure. The discussion covers negative funding, premium changes,…
This update reviews how competition has altered a cryptocurrency perpetual funding-rate arbitrage approach. It reports that positive entry premiums and extreme negative premiums had become less common, while slippage made attempts to capture premium…
This article presents a long-only dynamic grid framework for linear perpetual contracts that separates trade timing from total capital allocation. The grid sets entry and batch exit levels, with spacing constrained by minimum and maximum bounds, recent ATR…
This research examines dynamic delta hedging for Bitcoin options using adjustments informed by the implied volatility smile. It compares standard Black–Scholes delta with several smile-based and locally parameterized alternatives, including methods designed…
This document outlines a Python framework for managing a strategy across several cryptocurrency perpetual contracts. It organizes shared state by symbol and separates initialization, market-data updates, contract precision lookup, account and position…
APFF is a framework for ranking a changing universe of crypto perpetual futures and combining interpretable signals into a long-short portfolio. Its seed factors cover momentum, short-term reversal, funding, premium, and open-interest behavior.…
This tutorial explains a trend-following use of Keltner channels. It first describes a channel built around an average price and a range-based width, then presents an upgraded version using an exponential moving average for the center line and average true…
This article outlines a system that turns an analyst’s posts on X into potential trades in US equity perpetual futures. It proposes collecting posts through an RSS feed, asking a language model to identify explicitly named tickers and classify direction and…
The document explains how a strategy interface can use status-bar buttons to collect order settings for a selected instrument. Its example builds a table for several cryptocurrency perpetual futures, with a row-specific opening button that gathers order…
This tutorial presents examples for building simple market monitors with exchange data. One example repeatedly collects spot prices for Binance pairs, stores timestamped observations in rolling per-symbol windows, calculates percentage change from the oldest…
This article explains why OHLC candlestick backtests can mislead when evaluating high-frequency or multi-instrument strategies. Bars omit the timing of their highs and lows, do not show the best bid and ask needed to model matching, and cannot reliably…
This document outlines a crypto perpetual futures strategy that treats unusually large liquidation flows as directional signals. It builds a per-token baseline from historical liquidation amounts, compares recent activity with that baseline using a Z-score,…
This document describes a workflow for finding and trading funding rate spreads across centralized and decentralized perpetual exchanges. It collects cross-exchange rates, filters markets to configured venues, and keeps minute-level snapshots to score how…
The article develops a market-neutral rotation strategy for volatile crypto perpetuals. It ranks contracts using both price momentum and funding rates, goes long the strongest group and short the weakest with balanced notional, and estimates factor weights…