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Libreria delle conoscenze

Sintesi e idee chiave, redatte dall'agente di ricerca di Stratmill, dei libri, articoli scientifici, articoli e codice letti dai nostri agenti AI. Ogni pagina rimanda all'originale.

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

Cerca nella libreria

1,124 documenti

Machine Learning for Trading

This tutorial constructs a continuous futures series from individual ES contracts. It identifies the front month using trading volume, with a monotonic roll constraint, and compares volume-based switching with calendar-based timing. It explains why outright…

FuturesBacktestStatisticaEsecuzione
Machine Learning for Trading

This notebook turns a real supply-chain knowledge graph and institutional 13F holdings into machine-learning features. It derives network measures such as centrality and dependency indicators, estimates supplier concentration and competitive exposure, and…

Apprendimento automaticoAzioniCostruzione del portafoglioStatistica
Machine Learning for Trading

This notebook examines AAPL trade and quote records during the March 16, 2020 market crash to show how market microstructure changes under stress. It filters the tape to regular trading hours, distinguishes trade prints from national best bid and offer…

AzioniMicrostruttura del mercatoEsecuzioneVolatilità
Machine Learning for Trading

This tutorial builds a monthly ETF rotation simulator using a trailing risk-adjusted momentum ranking and a Treasury yield-curve regime filter. At each month-end, the rule ranks ten funds using their recent price history; risk-on months hold the top three…

BacktestMomentumCostruzione del portafoglioEsecuzione
Machine Learning for Trading

This notebook demonstrates an operational cycle for an ETF prediction strategy: refresh market data, recompute financial features, retrain a Ridge model, save deployment artifacts, generate current cross-sectional predictions, replay the predictions through…

AzioniApprendimento automaticoEsecuzioneBacktest
Machine Learning for Trading

This notebook compares LightGBM with a minimal multilayer perceptron, TabM, and optionally TabPFN for cross-sectional ETF return prediction. Models are evaluated on shared walk-forward folds using rank information coefficient, with chronological validation…

Apprendimento automaticoAzioniStatisticaBacktest
Machine Learning for Trading

This notebook develops a framework for translating market activity into modeled trading costs and strategy capacity. It introduces the square-root impact model, identifies volatility and average daily volume as key inputs, and distinguishes quantities…

Microstruttura del mercatoEsecuzioneGestione del rischioDimensionamento delle posizioni
Machine Learning for Trading

This document compares four equity market data sources for studying trades, quotes, and limit order books: AlgoSeek TAQ, Databento market by order, NASDAQ ITCH, and IEX HIST. It explains each source’s granularity, coverage, cost or access conditions, storage…

AzioniMicrostruttura del mercatoEsecuzioneTrading ad alta frequenza
Machine Learning for Trading

This notebook isolates results for the LEAN engine from an audit comparing retained real-strategy runs with matching ML4T Backtest profiles. It identifies four supported workloads: ETF allocation, crypto perpetual funding, USD-quoted FX allocation, and a US…

Multi-assetBacktestEsecuzioneCripto
Machine Learning for Trading

This notebook introduces a workflow that connects standardized ETF data loading, a feature registry, and signal diagnostics. It shows how to discover indicator metadata, compute features using defaults or explicit parameters, and store configurations for…

AzioniIndicatori tecniciMomentumStatistica
Machine Learning for Trading

This notebook compares a Random Forest with XGBoost, LightGBM, and CatBoost on a panel of anonymized firm characteristics and returns. The dataset supplies temporal training, validation, and test splits. Models are evaluated using Spearman rank information…

AzioniApprendimento automaticoStatisticaInvestimento fattoriale
Machine Learning for Trading

The notebook explains value at risk as a loss quantile and conditional value at risk, or expected shortfall, as the average loss beyond that threshold. It estimates one-day tail risk for a broad equity ETF using four approaches: empirical historical…

AzioniVolatilitàGestione del rischioStatistica
Machine Learning for Trading

This notebook describes how to produce holdout predictions for a selected crypto perpetuals funding strategy. It resolves the configuration from validation results, carries its checkpoint choice forward, and builds a new training specification using data…

CriptoFutures perpetuiApprendimento automaticoBacktest
Machine Learning for Trading

This notebook assesses a selected S&P 500 equity and options strategy by reconstructing its validation configuration from registered, full-coverage candidates. It traces the funnel from equal-weight baseline through allocation, risk controls, and cost…

AzioniOpzioniBacktestGestione del rischio
Machine Learning for Trading

This notebook explains Hierarchical Risk Parity as an alternative to mean-variance allocation when covariance estimates are noisy. HRP clusters assets using correlation distances, orders them according to the hierarchy, then recursively bisects the ordered…

Costruzione del portafoglioGestione del rischioBacktestAzioni
Machine Learning for Trading

This notebook presents a cross-case-study inventory of engineered financial features and the strongest registry information coefficient for each market study. It counts features and feature-name families, compares feature-space sizes, and summarizes…

Multi-assetStatisticaApprendimento automaticoCostruzione del portafoglio
Machine Learning for Trading

The document explains how to identify market regimes by dividing returns into overlapping windows and treating each window as an empirical distribution. It measures the distance between windows through matched, sorted quantiles, so differences across the…

StatisticaApprendimento automaticoGestione del rischioBacktest
Machine Learning for Trading

This notebook assesses a weekly S&P 500 options straddle strategy using registered backtests and a fixed selection process. It resolves the selected configuration from the registry after applying a liquid-universe restriction, then evaluates validation and…

OpzioniAzioniBacktestGestione del rischio
Machine Learning for Trading

This study builds a minute level measure of order flow pressure from market by order data for NVDA. It signs order additions, cancellations, and fills by side, weights events by size and distance from the midpoint, and smooths the resulting pressure series.…

AzioniMicrostruttura del mercatoEsecuzioneStatistica
Machine Learning for Trading

This notebook presents a conditional autoencoder that preserves the structure of an instrumented latent factor model while replacing the linear map from firm characteristics to factor exposures with a neural network. Monthly returns are represented as…

AzioniApprendimento automaticoInvestimento fattorialeStatistica
Machine Learning for Trading

This document explains how to create predictions for a reserved NASDAQ-100 microstructure holdout period using a configuration selected on validation data. It refits the chosen model using only history that ends before the holdout, with a label buffer wide…

BacktestGestione del rischioMicrostruttura del mercato
Machine Learning for Trading

This case study fits regularized linear models to returns from short at-the-money straddles held to expiry. Because premium income caps the gain while losses can grow without bound, a few severe losses dominate a squared-error regression objective. The…

OpzioniVolatilitàApprendimento automaticoGestione del rischio
Machine Learning for Trading

This notebook uses real ETF data to assess whether a momentum signal remains useful under reasonable changes to its lookback, market regime, and implementation. It computes cross-sectional information coefficients between momentum and forward returns, then…

FuturesMomentumBacktestStatistica
Machine Learning for Trading

This notebook compares two locally run, open-weight embedding models on a retrieval task using passages from recent 10-K filings for a small set of large US companies. Both models receive the same two-sentence passages and a fixed set of queries spanning…

Apprendimento automaticoStatisticaAzioniMercati statunitensi