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.
42 documents
This implementation models a spread as a collection of instrument legs, with separate multipliers for calculating its quoted price and translating spread quantities into leg quantities. It combines leg bid and ask prices, reversing which side is used for…
The document is a historical series of hourly candlestick observations for a BCH/USDT market. Each row records a timestamp, open, high, low, close, and traded volume, giving the basic inputs commonly used to inspect price movement, calculate technical…
The document contains historical ADA/USDT candlestick observations at half-hour intervals. Each row records a timestamp, open, high, low, close, and traded volume, allowing a researcher to inspect price movement and activity or use the series as an input to…
This strategy uses a fast and a slow moving average to trade a cryptocurrency futures contract. A bullish crossover opens a long position or reverses a short position; a bearish crossover opens short or reverses long. Signals use earlier completed bars…
The document shows a simple workflow for evaluating two futures strategies together. It runs separate historical simulations for an ATR-RSI strategy on an equity index contract and a Bollinger channel strategy on a metal contract. Each run specifies its own…
This document explains how to build a multi-contract strategy using synchronized bar data, per-leg targets, and order management. Its example computes the spread between two weighted contract prices, updates a rolling window, and uses Bollinger Bands to…
The document describes a two-leg spread strategy built around Bollinger Bands. It calculates a weighted price difference between two contracts, samples the spread on a five-minute schedule, and compares it with a rolling mean and standard deviation. A move…
This document is a daily candlestick dataset for the BTC/USDT market during 2019. Each row records a timestamp and the open, high, low, and close prices, together with traded volume. The visible entries span portions of the year, including early-year…
The document contains hourly open, high, low, close, and volume observations for the ADA-USDT market. The visible records begin in early May 2018 and continue through the end of December 2018, with gaps in the displayed sequence. The fields support basic…
This document explains the structure of a historical trading strategy backtester. It loads bar or tick records over a selected date range, initializes a strategy with a warm-up period, then replays the remaining data. The engine tracks simulated orders and…
The strategy applies a long-only moving average crossover to daily bars for a single stock. After enough bars are available to calculate both averages, it treats an upward cross of the shorter average over the longer one as an entry signal and buys when…
The document describes a graphical workflow for downloading historical bars, configuring a CTA strategy backtest, reviewing performance statistics, and inspecting trades on a candlestick chart. Data can come from a domestic market data service, an…
The document walks through preparing a Python environment, installing a trading framework, and launching its graphical interface. The example registers exchange gateways and applications for strategy execution, historical data recording, risk controls,…
The document explains a local simulator that routes orders and cancellations to a paper-trading engine instead of sending them to an external trading server. It supports limit, market, and stop orders, and uses quote-triggered matching: for example, a buy…
This document describes a framework for building trading strategies around market-data and order-event callbacks. A strategy can receive tick, bar, trade, order, and stop-order updates; load historical bars or ticks during initialization; and query or…
This guide explains a workflow for researching CTA strategies with historical market data. It covers obtaining and storing data, configuring a backtest with a strategy, date range, slippage, fees, contract multiplier, tick size, and starting capital, then…
This document describes a charting utility for displaying market candles and volume alongside technical indicators. It organizes the view into a main price panel, a volume panel, and a secondary indicator panel, and includes a line for the latest traded…
This document describes the data model and calculations behind a synthetic multi-leg spread. Each leg stores its market quotes, contract details, and position state. Configurable price multipliers define the spread price, while trading multipliers define how…
The document presents a workflow for evaluating individual trades from a Turtle-style strategy backtest on an hourly Bitcoin instrument. It configures a backtest with a historical date range, fees, slippage, contract size, tick size, and starting capital,…
This strategy combines Bollinger-style price bands with the Commodity Channel Index (CCI) to generate directional entries on 15-minute bars. It calculates a simple moving average and standard deviation over a configurable lookback, then places a stop entry…
This spot strategy generates signals from a fast and a slow moving average. It identifies a bullish crossover using completed bar values rather than the current bar, which is intended to avoid signals that flicker while a bar is forming. A bullish cross…
This strategy uses Bollinger-style price bands to enter long or short positions when a bar reaches beyond the upper or lower band. It adds a directional filter based on the difference between the latest close and a close from an earlier lookback: positive…
This guide explains a market-data recorder that subscribes to selected instruments and saves live tick or one-minute bar data to a database. The stored history can then be reviewed in a data-management interface, used in historical backtests, or loaded to…
This example shows a workflow for backtesting an ATR-RSI strategy on one-minute futures data. The setup specifies the contract, date range, transaction costs, slippage, contract size, tick size, and starting capital, then loads data, runs the simulation,…