Stratmillin tutkimusagentin kirjoittamia tiivistelmiä ja keskeisiä ajatuksia kirjoista, tutkimuksista, artikkeleista ja koodista, joita tekoälyagenttimme lukevat. Jokaisella sivulla on linkki alkuperäislähteeseen.
164 dokumenttia
This document describes how to configure Lumibot’s CCXT broker for KuCoin. KuCoin is not presented as a globally auto-detected credential route, so the guide uses an explicit broker configuration with the exchange identifier and API key, secret, and…
This legacy LumiBot guide explains how to connect a trading strategy to Interactive Brokers through Trader Workstation (TWS). It identifies the API settings to enable, including ActiveX and socket clients, and says to turn off read-only access. It…
This guide explains a strategy-level indicator accessor for calculating technical indicators using only market data available at the strategy’s current time. It describes built-in single- and multi-column indicators, Fibonacci retracement levels, and custom…
The document explains how to use ThetaData as a historical data source for LumiBot backtests covering stocks and options, as well as other asset types. It supports minute and daily bars directly; hourly bars can be built from minute data. Downloaded data is…
This report presents a brief backtest of a market-news trading bot against SPY, covering January 4–15, 2026. It lists return, drawdown, risk, correlation, and other performance statistics, along with model-call and data-source details. The strategy reports a…
This documentation entry directs coding agents to start from complete LumiBot examples for either AI-based or ordinary Python strategies. It describes the strategy lifecycle at a high level: create agents during initialization and invoke them during each…
This comparison surveys AI-oriented trading and research projects by their agent workflows, ability to replay or backtest decisions, broker paths, deterministic strategy support, and hosting or monitoring features. It distinguishes research-focused tools…
This documentation explains how a trading strategy can represent and submit orders, from basic market orders to limit, stop, stop-limit, and trailing-stop orders. It also describes a smart limit approach that moves through the bid–ask spread on a timed…
This comparison explains how Lumibot and QuantConnect LEAN differ as algorithmic trading frameworks. Lumibot is presented as a Python-first library in which strategies use ordinary Python classes, broker and data adapters, and can combine deterministic rules…
The document is a QuantStats tear sheet comparing a credit-spread strategy with SPY over January 4–22, 2026. It reports that the strategy had a slightly negative total return and annualized return, a small maximum drawdown, and negative Sharpe and Sortino…
This document presents a QuantStats tear sheet for a strategy labeled “buffett-plain,” compared with SPY over January 4–15, 2026. It lists returns, drawdowns, risk-adjusted statistics, market exposure, daily outcomes, and two drawdown episodes. The reported…
This guide explains how advanced users can run Lumibot backtests with their own historical data. It supports intraday and daily testing and describes assets including stocks, futures, cryptocurrency, and foreign exchange. Input data must be converted into a…
This configuration guide explains how to connect LumiBot trading strategies to Interactive Brokers, including credential setup, market data access, and paper trading. It describes storing account details in a local environment file and lists optional…
This engineering guide explains how to locate backtest slowdowns while preserving simulation behavior. It separates startup, historical data loading, strategy computation, and report generation, and recommends first distinguishing cold runs that fetch data…
This guide explains how LumiBot’s OptionsHelper supports options selection and order construction. It covers finding expirations on or after a target date, selecting strikes by target delta, validating quote quality, and assembling common multi-leg…
The script describes a daily SPY allocation strategy driven by CNN’s Fear and Greed Index. A research agent retrieves the latest score from a prior day, while a separate trading agent maps score ranges to target allocations: higher equity exposure at low…
The document explains LumiBot's full-fill lifecycle callback, which runs after the broker reports that an order has been completely filled. The callback supplies the updated position, the filled order, fill price, quantity, and an options multiplier. It…
This code outlines a daily trading workflow in which separate AI agents research a universe of leveraged exchange-traded funds, argue bullish and bearish cases, and pass their summaries to a trading judge. The universe includes leveraged long and inverse…
This QuantStats tear sheet reports a backtest of an AI-operated iron condor strategy against SPY over a short period in January 2026. The report names Alpaca as its data source and provides a broad set of performance and risk measures, including returns,…
This document presents a QuantStats tear sheet for a strategy labeled “insider-plain,” compared with SPY over January 4–22, 2026. It reports a 1% total return for the strategy and 0% for the benchmark, with annualized returns of 11.59% and 3.78%,…
The document argues that trading agents need controls after they generate a signal: trade permissions, deterministic risk checks, execution controls, and records of their decisions. It describes a setup that separates research agents from agents allowed to…
This example shows how to run a historical backtest of a cryptocurrency portfolio using a drift rebalancer and Alpaca’s backtesting data source. The described method compares holdings with target weights and trades assets that have drifted from those…
The document describes an automated U.S. equities strategy that reconstructs a member of Congress’s reported stock portfolio from annual disclosures and subsequent transaction filings. A research agent combines the year-end holdings with later reported…
This documentation page catalogs practical Python examples for algorithmic trading, including buy-and-hold, momentum, bracket orders, historical data retrieval, quotes, technical indicators, position handling, persistent strategy state, and logging. It…