Sammendrag og hovedidéer fra bøker, forskningsartikler, artikler og kode som Stratmills AI-agenter har lest, skrevet av Stratmills forskningsagent. Hver side lenker til originalen.
38 dokumenter
This guide explains how a trading platform can connect to Ethereum and TRON through Web3 exchange objects and use RPC calls and smart contract methods. It covers node and key configuration, ABI registration, wallet and token queries, transfers, allowances,…
This guide explains a formula tool for rapidly calculating and checking trading ideas using expressions based on publicly available WorldQuant Alpha101 methods. It lists arithmetic and conditional syntax, cross-sectional ranking, lagging, moving averages,…
This guide explains how programs can call FMZ’s extension API to retrieve account and robot information, issue commands, and manage robot states. It covers API key creation and per-method permissions, response and status codes, request parameters, and an…
This guide explains how interactive controls send commands to a running trading strategy. Controls can carry numbers, booleans, strings, dropdown selections, or button actions; strategy code retrieves the resulting messages and can use them to change…
This guide surveys the languages and visual tools available for building trading strategies on FMZ, including JavaScript, TypeScript, Python, Rust, C++, My language, Pine, Blockly, and Workflow. It describes selected platform features: JavaScript…
This guide explains FMZ’s simulated-tick and real-tick backtesting modes. Simulated ticks are generated from candlestick data, while real-tick mode replays recorded tick data and can include depth and trade records. The guide describes the trade-off between…
This reference describes the fields used by FMZ trading data structures, including market trades, tickers, candlestick records, orders, and conditional-order configurations. It covers identifiers, timestamps, prices, quantities, volume, open interest, order…
The guide explains how FMZ handles sensitive account details and strategy parameters. It says these values are encrypted in the browser and stored as encrypted data, with decryption available on the user's private device. It also cautions against exposing…
This platform guide describes how to select and trade cryptocurrency options through a common trading interface. It outlines contract selection, market-data retrieval, order placement and cancellation, and position queries for Deribit and OKX. It also lists…
The guide outlines a basic structure for automated strategies in JavaScript, Python, Rust, and C++. A main loop repeatedly runs strategy logic, while a sleep interval controls polling frequency in live trading and playback speed in backtests. Its examples…
This guide explains a key difference between JavaScript and C++ strategies on the FMZ Quant Trading Platform: how API calls signal failure. In JavaScript, a failed ticker request returns null, so a strategy can test whether the result exists before using it.…
This guide collects exchange-specific implementation details for brokerage and cryptocurrency connections. For Futu, it explains market-specific stock symbol formats, account market identification, and the effect of disabling cached account and position data…
The guide explains how to connect an exchange to the FMZ Quant Trading Platform when FMZ has not already integrated that exchange’s API. It describes writing a general protocol plugin that communicates with the exchange and the FMZ Quant docker program. The…
The guide describes two ways to protect exchange credentials used by the FMZ trading platform. It says sensitive account data and encrypted strategy parameters are encrypted in the browser and stored encrypted. It also advises keeping sensitive material out…
This guide explains how FMZ evaluates trading strategies on historical market data. It contrasts simulated Tick backtests, which construct intrabar price events from candles, with live-data Tick backtests, which replay collected tick, depth, and trade…
This guide describes an FMZ tool for evaluating trading ideas with time-series expressions modeled partly on publicly available Alpha101 calculations. It lists arithmetic and logical operators, transformations such as moving averages and differences,…
This guide explains how FMZ strategy interfaces can send commands to a running trading program. It covers numeric, Boolean, text, dropdown, and button controls, and describes how supported languages can receive their messages through a command-reading…
This guide explains how to use FMZ to access cryptocurrency options on Deribit, OKX, Huobi, Bybit, Aevo, and Gate.io. It describes selecting an options contract, retrieving market data, placing and canceling orders, checking positions, and finding contract…
This guide explains how FMZ’s template library feature lets users package reusable functions for strategies. It describes creating a library in JavaScript, Python, C++, or Blockly Visual mode, and shows how exported functions can be called from a strategy…
This guide explains how to use FMZ Trading Terminal plugins to support manual or semi-automated trading. A plugin runs a code snippet on a selected Docker instance and can return data tables or charts. The examples include displaying an exchange order-book…
This FMZ guide outlines a continuously running strategy loop across JavaScript, Python, Rust, and C++. It shows calling strategy logic repeatedly with a configurable sleep interval, which controls polling frequency in live trading and playback speed in…
This platform guide surveys the ways to build trading strategies in FMZ, including JavaScript, TypeScript, Python, Rust, C++, Pine, a domain-specific language, Blockly, and visual workflows. It explains selected development features: JavaScript asynchronous…
This guide groups common causes of failures in live trading strategies. It covers syntax and runtime errors, excessive memory use, unmanaged asynchronous requests, deep recursion, and errors returned by exchange interfaces. A recurring practical lesson is to…
This platform guide explains how FMZ defines live trading: a strategy instance connected either to an exchange’s production environment or to its simulation environment. Starting an instance requires a saved strategy, a deployed platform host, and at least…