Knowledge library

This article describes a modification to random forest models that lets an investor express factor preferences directly in the tree-building process. Selected factors are forced to drive splits near the top of each decision tree, increasing their influence.…

This exchange asks whether MACD components and a short-window swing-volatility feature used in model training and prediction contain future information. The response says these factors are calculated from historical data, implying that the indicators…

This script outlines a FinRL training workflow for stock trading. It reads training data, derives the stock universe size and state-space dimensions, and configures a stock-trading environment with technical indicators, transaction costs, initial capital,…

This promotional fireside-chat recap describes OctoBot’s use of AI to turn natural-language requests into crypto trading setups. Examples include selecting a crypto basket, creating a dollar-cost averaging plan, configuring a grid strategy, and generating a…

The post describes a problem loading a saved StockRanker model into a live trading environment. The author reports that prediction fails with a JSON parsing error, even though the model works on the platform in the development environment. This is a report…

This Chinese-language announcement outlines an advanced course on developing machine learning cross-sectional multi-factor strategies with VeighNa. Its subject matter includes factor data preparation, feature cleaning, target selection, model training and…

This notebook walks through a minimal Qlib reinforcement learning setup, connecting a simulator, state and action interpreters, a reward function, a policy, a dataset, and training and backtest workflows. Its simulator runs for a fixed number of steps,…

This survey introduces supervised, unsupervised, semi-supervised, and reinforcement learning, then outlines common methods such as neural networks, support vector machines, nearest neighbors, clustering, and Bayesian networks. It places machine learning in…

This survey introduces supervised, unsupervised, semi-supervised, and reinforcement learning, then outlines common methods such as neural networks, support vector machines, nearest neighbors, clustering, and Bayesian networks. It places machine learning in…

The document presents KCI Volatility Distance as an indicator intended to measure directional strength and market conditions. It describes using its output to confirm momentum, supply a feature to a machine learning model, adjust grid spacing, scan multiple…

This Qlib documentation explains how forecast models produce stock prediction scores and how to train and run a model independently or within an automated workflow. It describes the base model interfaces, including support for fine-tuning, and gives a…

This paper guide summarizes research on convolutional networks, model efficiency, and image segmentation. It describes channel attention through feature recalibration, depthwise separable convolutions for reducing computation, and encoder-decoder or…

This brief forum exchange concerns an SQL error encountered after adding a z-score standardization function to factor data. The reported issue is that a query joining tables cannot determine which table supplies a column named date. The response recommends…

This document describes an Expert Advisor built around a simple neural network and two indicators: Laguerre_PlusDi and SilverTrend_Signal. Its setup relies on three sequential optimization passes, each activated by setting a Pass parameter and optimizing a…

The article explains how a multi-asset daily market-data panel can be turned into model-ready factor and label tables. It separates the workflow into defining train, validation, and test date ranges; adding feature expressions and a future-return label;…

This document introduces two high-frequency trading examples: handling a dataset for reinforcement learning and predicting price trends. It explains that the dataset is represented by a Qlib DatasetH object, which can be serialized to disk and reloaded.…

The discussion explains a rolling sequence window as a way to assemble recent observations into a single training example. For instance, a model can receive a window of past factor values spanning several days, with the window advancing through time to…

This tutorial introduces recurrent neural networks as models for sequential data, then explains long short-term memory networks as a design intended to address the difficulty ordinary RNNs have retaining information across long sequences. It describes the…

This introductory tutorial explains machine learning concepts and linear regression with finance examples. It distinguishes features from labels, outlines training, validation, and test data, and contrasts supervised with unsupervised learning,…

This research review applies conditional generative adversarial networks (cGANs) to multi-asset allocation. It conditions the model on recent asset-return sequences and random inputs to generate possible future paths, then estimates expected returns and…

This short Chinese-language post introduces SciPy as a Python library for statistical and optimization techniques, following a brief mention of NumPy and pandas as common tools for data work. Its stated focus is using SciPy for statistical analysis. For a…

This brief Chinese-language entry describes a lecture on deep reinforcement learning by researcher Matteo Hessel. It introduces general value functions (GVFs) as auxiliary tasks and mentions methods for handling scaling problems in reinforcement learning…

This weekly report compares stock selection portfolios built with several artificial intelligence methods across broad Chinese A-share and index-constituent universes. It separates results for unconstrained selection, industry-neutral portfolios, and…

This extended Commodity Channel Index (CCI) indicator offers several ways to alter the calculation. It can use a regular CCI or adapt its behavior with a Vertical Horizontal Filter (VHF). Before calculating CCI, users may apply a simple, exponential,…