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Biblioteca de conhecimento

Resumos e ideias principais, escritos pelo agente de investigação da Stratmill, dos livros, artigos científicos, artigos e código consultados pelos nossos agentes de IA. Cada página inclui uma ligação para o original.

Quant Q&A
20,364 documentos
SuperMind
12,226 documentos
OKX Learn
8,431 documentos
Strategy library
7,910 documentos
MQL5 code base
7,090 documentos
BigQuant
3,481 documentos
Bitget Academy
3,298 documentos
MQL5 articles
3,012 documentos
TradingView scripts
1,976 documentos
ProRealCode
1,507 documentos
Deribit Insights
1,232 documentos
Machine Learning for Trading
1,124 documentos
arXiv papers
1,033 documentos
Amberdata research
766 documentos
FMZ forum
682 documentos
FMZ digest
662 documentos
vn.py community
560 documentos
QuantInsti blog
511 documentos
Galaxy Research
340 documentos
QuantStart
246 documentos
Stratmill research code
219 documentos
Robot Wealth
195 documentos
NautilusTrader
191 documentos
Hummingbot docs
181 documentos
Paradigm research
175 documentos
Lumibot
164 documentos
Kraken Learn
163 documentos
Biblioteca de cursos de quant
157 documentos
OctoBot
152 documentos
Cryptohopper blog
144 documentos
Systematic trading blog (Rob Carver)
132 documentos
Qlib
116 documentos
TqSdk
86 documentos
Quantpedia
86 documentos
Hyperliquid docs
79 documentos
Freqtrade
68 documentos
Hudson & Thames
62 documentos
Awesome Systematic Trading
61 documentos
backtrader
54 documentos
vn.py
50 documentos
Binance API docs
45 documentos
Aulas Quantopian
45 documentos
FMZ guides
38 documentos
pysystemtrade
34 documentos
Freqtrade docs
32 documentos
quant-trading
31 documentos
FinRL
28 documentos
Zipline
22 documentos
FMZ live strategies
21 documentos
Jesse
17 documentos
pyfolio
16 documentos
WonderTrader
14 documentos
Alphalens
14 documentos
backtesting.py
11 documentos
Technical Analysis
9 documentos
QTPyLib
8 documentos
QuantRocket
7 documentos
Lumibot strategies
7 documentos
Awesome Quant
1 documentos

Pesquisar na biblioteca

1,124 documentos

Machine Learning for Trading

This utility describes how to validate a migration that changes backtest identifiers without rerunning the underlying computations. It scans database schemas to discover columns containing registered addresses, including embedded references in text…

Testes históricosEstatística
Machine Learning for Trading

This analysis assesses whether signals selected in multiple trading case studies appear dependable enough for further use. It compares validation information coefficients (IC) with holdout evidence, examines stability and reproducibility, and contrasts…

Aprendizagem automáticaEstatísticaTestes históricosGestão do risco
Machine Learning for Trading

This tutorial surveys features built from asset price and volume histories, including returns across horizons, trend and reversal measures, several volatility estimators, volatility regimes, liquidity, tail risk, and cross-sectional normalization. It…

Indicadores técnicosMomentumVolatilidadeAprendizagem automática
Machine Learning for Trading

This notebook measures how an S&P 500 options strategy's validation performance changes under different assumed fractions of the quoted spread paid. It selects one baseline strategy per model family, then reruns each across spread-cost assumptions and both a…

OpçõesExecuçãoTestes históricosGestão do risco
Machine Learning for Trading

This document presents a reusable state machine for automated trading halts, with closed, open, and half-open states. It applies the same transition logic to four risks: account drawdown, daily loss, consecutive losing sessions, and system latency. After a…

Gestão do riscoExecuçãoTestes históricos
Machine Learning for Trading

This notebook explains instrumented principal component analysis (IPCA) for an equity panel with option-surface features. Unlike ordinary PCA, which estimates each stock's exposures from its return history, IPCA models exposures as a shared linear function…

AçõesOpçõesInvestimento em fatoresAprendizagem automática
Machine Learning for Trading

This notebook generates predictions for a previously untouched ETF holdout window using the configuration selected on validation data. It rebuilds the training specification to end one label horizon before the window opens, refits the model, preserves the…

Testes históricosGestão do riscoEstatística
Machine Learning for Trading

This notebook outlines a pipeline for extracting supplier, customer, and competitor relationships from company 10-K filings and storing them as a Neo4j knowledge graph. A local language model proposes structured subject-predicate-object triples, after which…

AçõesAprendizagem automáticaEstatísticaGestão do risco
Machine Learning for Trading

This dataset guide compares two sources of US equity tick data for order book and market microstructure research. Databento market-by-order records provide preprocessed add, modify, cancel, and trade events, while NASDAQ ITCH supplies a raw exchange feed…

AçõesMicroestrutura de mercadoExecução
Machine Learning for Trading

This document describes registry checks for deciding whether to reuse or rerun model training and backtests. Runs are identified by hashes derived from their specifications; a run is reusable only when its expected artifacts are present. Missing pieces mark…

Testes históricosAprendizagem automáticaEstatística
Machine Learning for Trading

This notebook explains why a generic target-weight risk overlay cannot govern the described S&P 500 short-straddle strategy. The options engine allocates fixed capital fractions to weekly cohorts and normalizes weights within each cohort; scaling those…

OpçõesGestão do riscoTestes históricosAvaliação de derivados
Machine Learning for Trading

This notebook demonstrates fine-tuning a transformer for financial named entity recognition, turning text spans into structured organization, person, money, date, and percentage fields. It introduces BIO boundary labels and explains how word-level…

Aprendizagem automáticaEstatísticaSentimento de mercadoAções
Machine Learning for Trading

This notebook studies gradient boosting for cross-sectional prediction of CME futures returns. It varies tree capacity through leaf-count profiles and compares squared-error, absolute-error, and Huber objectives, which differ in how strongly extreme…

FuturosMatérias-primasAprendizagem automáticaTestes históricos
Machine Learning for Trading

This notebook explains three ways to turn minute-level NASDAQ-100 data into model-based features: a rolling HAR regression for variance forecasts and forecast errors, a Fourier transform for recent volume patterns, and depth-two path signatures for the order…

AçõesVolatilidadeMicroestrutura de mercadoEstatística
Machine Learning for Trading

This notebook adds a temporal convolutional network (TCN) to a comparison of sequence models for forecasting cryptocurrency perpetual funding premiums. Causal padding ensures each position uses only current and earlier observations, while dilated…

CriptoativosFuturos perpétuosAprendizagem automáticaTestes históricos
Machine Learning for Trading

This notebook compares four ways to retrieve passages from a corpus of company filing text: BM25, dense embeddings, reciprocal rank fusion (RRF), and a cross-encoder reranker. RRF combines ranked lists rather than raw scores, so it can join lexical and…

Aprendizagem automáticaEstatísticaAçõesMercados dos EUA
Machine Learning for Trading

This document presents an end-to-end forecasting architecture that combines a research panel, an aggregate forecast, an adversarial debate, and a supervisor. The supervisor identifies disagreements, searches for clarifying evidence, and proposes a…

Aprendizagem automáticaEstatísticaGestão do risco
Machine Learning for Trading

This case study evaluates daily cross-sectional signals across a broad US stock universe and lays out a long research pipeline, from point-in-time data and engineered features through model comparison, portfolio construction, costs, and holdout assessment.…

AçõesAprendizagem automáticaMomentumReversão à média
Machine Learning for Trading

This module estimates parameters for simulated crypto markets used in reinforcement-learning environments. It loads hourly perpetual-market data for a selected symbol, computes close-to-close log returns, and fits a GARCH(1,1) volatility model. If the…

CriptoativosFuturos perpétuosVolatilidadeMicroestrutura de mercado
Machine Learning for Trading

This notebook organizes cross-case-study comparisons of LightGBM models, using linear models and TabM as reference families where available. It evaluates configurations across tree sizes, regression losses, checkpoints, and classification objectives. The…

Aprendizagem automáticaEstatísticaTestes históricos
Machine Learning for Trading

This document describes a Gymnasium environment for training an agent to liquidate a position over a fixed horizon. The observation combines remaining inventory and time with spread, market depth, volatility, and a normal or stressed regime. A continuous…

Aprendizagem automáticaExecuçãoMicroestrutura de mercadoVolatilidade
Machine Learning for Trading

This notebook compares LightGBM with an MLP, TabM, and, when available, TabPFN for predicting ETF forward returns from tabular features. Models are evaluated on shared chronological walk-forward folds, with rank information coefficient and training time as…

AçõesAprendizagem automáticaEstatísticaTestes históricos
Machine Learning for Trading

The document describes a validation-stage comparison of fixed and trailing stop rules for FX strategies. For each return label, it selects a parent strategy from a sealed cohort of signal and allocation results, then varies the declared position-level risk…

CâmbioGestão do riscoTestes históricosDimensionamento de posições
Machine Learning for Trading

This notebook compares ordinary least squares with Ridge, LASSO, and Elastic Net for predicting 21-day forward returns across ETFs. The models use the same declared walk-forward folds, with a purge gap matched to the label horizon. Each fold clips feature…

AçõesAprendizagem automáticaEstatísticaTestes históricos