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Kennisbibliotheek

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.

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

Doorzoek de bibliotheek

1,124 documenten

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…

BacktestenStatistiek
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…

Machine learningStatistiekBacktestenRisicobeheer
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…

Technische indicatorenMomentumVolatiliteitMachine learning
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…

OptiesOrderuitvoeringBacktestenRisicobeheer
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…

RisicobeheerOrderuitvoeringBacktesten
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…

AandelenOptiesFactorbeleggenMachine learning
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…

BacktestenRisicobeheerStatistiek
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…

AandelenMachine learningStatistiekRisicobeheer
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…

AandelenMarktmicrostructuurOrderuitvoering
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…

BacktestenMachine learningStatistiek
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…

OptiesRisicobeheerBacktestenPrijsbepaling van derivaten
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…

Machine learningStatistiekMarktsentimentAandelen
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…

FuturesGrondstoffenMachine learningBacktesten
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…

AandelenVolatiliteitMarktmicrostructuurStatistiek
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…

CryptoPerpetuele futuresMachine learningBacktesten
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…

Machine learningStatistiekAandelenAmerikaanse markten
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…

Machine learningStatistiekRisicobeheer
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.…

AandelenMachine learningMomentumTerugkeer naar het gemiddelde
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…

CryptoPerpetuele futuresVolatiliteitMarktmicrostructuur
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…

Machine learningStatistiekBacktesten
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…

Machine learningOrderuitvoeringMarktmicrostructuurVolatiliteit
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…

AandelenMachine learningStatistiekBacktesten
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…

ValutahandelRisicobeheerBacktestenPositiegrootte
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…

AandelenMachine learningStatistiekBacktesten