Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.
32 documenten
This guide explains how to turn a trading idea into a Freqtrade strategy, from generating a template to defining indicators, entry and exit signals, stop losses, and optional position adjustments. It describes how Freqtrade represents candle data in pandas…
This documentation explains how Freqtrade strategy callbacks complement vectorized indicator and signal functions. Callbacks run when needed, often repeatedly during live trading or at each simulated candle, so the guidance warns against costly calculations…
The document explains lookahead bias: a backtest can accidentally use future candle data because the full historical dataframe is loaded before indicators and signals are calculated. This can make results appear unrealistically strong. It describes an…
This documentation explains how a Freqtrade instance can act as a producer, broadcasting analyzed dataframes and whitelists over a message websocket, while one or more consumer instances reuse that information. The approach lets consumers access indicators…
This documentation explains how to configure FreqAI within a Freqtrade configuration and strategy. It outlines core settings for training and backtesting periods, model identification, timeframes, correlated pairs, shifted candles, indicator periods, labels,…
This guide explains how FreqAI trains and deploys adaptive machine-learning models in live or dry trading and in historical backtests. Live operation can retrain models as capacity permits, use the latest trained model for predictions, and apply limits on…
This reference explains how Freqtrade handles pair naming, fees, and strategy execution. Spot pairs use a base and quote currency, while futures pair names also identify the settlement currency. Profit calculations include fees: simulations use the…
This guide explains advanced ways to configure strategy hyperoptimization in Freqtrade. It shows how to define a custom loss function, which receives trade results and backtest context and returns a score where lower values are preferred. The example…
This documentation explains how a trading bot builds the set of markets it can trade. Pairlist handlers can start from a static whitelist or dynamically select pairs by measures such as volume or percentage change; subsequent filters can remove or reorder…
This guide explains static and trailing stop losses, including trailing stops that switch to a tighter loss allowance after a profit threshold or begin trailing only after a specified offset. It also covers exchange-placed stops, comparing market orders,…
This document outlines interface and configuration changes for upgrading Freqtrade strategies from version 2 to version 3, especially when adding short trades or leverage. It maps older buy and sell signals to entry and exit terminology, including renamed…
Recursive analysis helps check whether indicator values depend materially on how many startup candles are available. Recursive formulas use prior values, so an indicator calculated over the full backtest history may differ from one calculated in a dry or…
This document describes a modified relative strength index that replaces the usual Wilder-style smoothing average with Alan Hull’s moving average. It also applies price filtering before calculating the indicator, making it a broader alteration of RSI than…
This reference lists configuration options for FreqAI, Freqtrade's machine-learning feature. General settings cover rolling training and inference windows, model identification and persistence, retraining frequency, model expiration, and prediction…
This Freqtrade documentation page describes strategy customization features beyond basic entry and exit signals. It explains how to store small JSON-serializable values persistently on individual trades, access analyzed candle data in callbacks, and use…
This reference explains the Trade object used by Freqtrade to represent a persisted position and the Order objects attached to it. It catalogs fields for pair, direction, entry and exit rates, stake and asset amounts, timestamps, profit, leverage, order…
This documentation explains how to define model inputs and prediction targets in FreqAI strategies. It distinguishes base features, such as price indicators, volume, and time variables, from configuration-driven expansions across periods, timeframes, shifted…
The document explains how an automated trading system handles spot, margin, and futures modes. Spot trading uses unleveraged long positions, while margin borrows capital and futures trade derivative contracts that may incur funding payments. It distinguishes…
This reference summarizes exchange-specific behavior relevant to configuring an automated cryptocurrency trading system. It compares supported spot and futures markets, margin modes, and available on-exchange stop orders, then discusses API rate limits,…
This documentation page catalogs Freqtrade features and settings that have been deprecated or removed, then explains migration implications for strategies and stored market data. It covers command-line options, pairlist configuration, strategy interfaces,…
This guide outlines a notebook workflow for debugging and analyzing a Freqtrade strategy. It loads historical candles for a selected pair and timeframe, runs the strategy to inspect generated entry signals, and explains that signal counts do not equal…
This documentation describes Freqtrade’s plotting commands for viewing price candles, volume, strategy indicators, and trades from a database or backtest export. The dataframe plot can show price-scale indicators such as moving averages alongside separate…
This documentation page explains how to run Freqtrade backtests on historical OHLCV data, select a strategy, timeframe, date range, trading pairs, starting balance, stake settings, fees, and output format, and compare multiple strategies in one run. It…
FreqAI is presented as an open source framework for training machine learning models to forecast market targets from user defined indicators. Users supply features and future looking labels; the framework trains a model for each listed trading pair and…