Kokkuvõtted ja põhiideed raamatutest, teadustöödest, artiklitest ja koodist, mida meie AI-agendid loevad. Need on koostanud Stratmilli uurimisagent. Igal lehel on link originaalile.
662 dokumenti
The document outlines three rule-based trading templates and frames them as stages of analysis: summarizing past prices, applying trading rules, and interpreting market positioning. The periodic model combines a moving average, a prior-high or prior-low…
This article presents a short-horizon strategy for trading two related crypto futures contracts. Its motivating example is a claimed brief lag between TRUMP and MELANIA: when one contract moves sharply, the strategy looks for a difference in their candle…
This tutorial explains how to connect TradingView alerts to an automated trading robot on the FMZ platform. It uses FMZ’s extended API to send a command to a selected robot, and describes restricting the API key to the command interface and encoding the…
This tutorial presents a Donchian Channel trend-following method based on the highest high and lowest low over a rolling lookback period. The midpoint is the average of those two channel boundaries. A close above the upper boundary opens a long position,…
This article outlines a framework for copying positions from one leader account to multiple follower accounts and products. It uses a leader-subscriber design: the leader tracks its positions, detects additions, removals, or size changes, then notifies…
This article explains how to run a Python strategy whose main logic resides in a local file on the machine hosting an FMZ trading robot. Its example uses a Dual Thrust futures strategy. One approach imports a local module and passes in the platform’s…
This tutorial shows how to assemble a basic spot cryptocurrency strategy with visual programming blocks. It tracks a reference price and buys when the market rises by a threshold, using a share of the account’s quote currency; after a threshold decline, it…
This experiment proposes a live football market portfolio that combines a favored team’s win contract with two low-probability protective score paths, such as a drawless score and a narrow underdog win. The basket aims to earn by closing early if the…
The document describes a thermostat strategy that switches between range trading and trend following according to a Choppiness Market Index style measure. The index compares the net change in closing price over a lookback period with the period’s high-low…
This tutorial describes a Bitcoin trading environment for reinforcement learning using OpenAI Gym and a PPO agent from Stable Baselines. The environment exposes recent OHLCV data alongside account history, permits buy, sell, or hold actions at fractional…
The document introduces FMZ’s visual workflow system as a way to connect market data, analysis, signal generation, risk controls, and order execution. It describes a modular approach in which users combine prebuilt nodes, external data requests, AI analysis,…
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…
This guide explains how to update parameters across many FMZ trading bots using the platform’s extension API. It first retrieves stopped or errored bots for a specified strategy, then fetches each bot’s current configuration so existing exchange connections,…
This beginner guide explains the building blocks of programmatic cryptocurrency trading. It distinguishes an exchange account from the account sections within an exchange, describes how an API lets software retrieve data and submit orders, and explains that…
This tutorial develops a simple spot arbitrage design that buys on the exchange with the lower price and sells on the one with the higher price. It explains practical requirements for implementing the idea: minimum and maximum order sizes, exchange price and…
This tutorial introduces block based programming for building trading strategies without writing conventional code. It explains how visual modules represent logic as connected blocks, then walks through creating a simple output program and a recurring…
This tutorial presents reusable M-language components for quantitative strategies, including percentage price changes, new highs, price-and-volume surges, narrow ranges, moving-average alignment, prior-high locations, and price gaps. It also introduces…
The document explains Kaufman’s Adaptive Moving Average (KAMA), which adjusts its responsiveness using an efficiency ratio based on net price movement relative to total movement over a lookback period. Low efficiency makes the average smoother in choppy…
This article describes upgrades to a cryptocurrency spot arbitrage strategy operating across two exchanges. It adds separate spread thresholds for trading in each direction, uses order-book bid and ask prices with a slippage allowance, and caps hedge size by…
This document revisits a multi-coin Binance futures strategy that trades each altcoin against a basket-level index of altcoin-to-BTC prices. Positions are scaled to the deviation from the cross-sectional mean, with an exchange simulation tracking margin,…
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…
The document describes how a backtest advances through historical data and compares bar-based simulation with two tick-based approaches. In a traditional on-bar test, the strategy receives one decision point per candle, typically using its close, while…
The document presents a trend-oriented trading signal built from the slope of a smoothed midpoint between the rolling high and low. It first calculates the high and low over a lookback window, averages them, smooths that series with a moving average, and…
The document explains a market-neutral portfolio approach that ranks a universe of related assets, buys equal-sized positions in the highest-ranked group, and shorts the lowest-ranked group. Its returns depend on whether the ranking factor separates future…