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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
Alphalens
14 documenti
WonderTrader
14 documenti
backtesting.py
11 documenti
Technical Analysis
9 documenti
QTPyLib
8 documenti
QuantRocket
7 documenti
Lumibot strategies
7 documenti
Awesome Quant
1 documenti

Cerca nella libreria

1,124 documenti

Machine Learning for Trading

This notebook describes fitting PatchTST to one-minute NASDAQ-100 data to predict returns over several forward horizons. The model groups consecutive observations into patches and applies attention across them, reducing the number of items compared while…

AzioniApprendimento automaticoBacktestStatistica
Machine Learning for Trading

This notebook checks whether historical ETF data can support a monthly ranking strategy before fitting a model or making forecasts. It tests the tradable universe using prior-year liquidity, counts eligible funds on rebalance dates, converts per-share…

Multi-assetBacktestEsecuzioneGestione del rischio
Machine Learning for Trading

This notebook compares ways to allocate capital across US equity positions while holding the model, checkpoint, rebalance dates, and selected stocks fixed. It examines weights based on prediction strength, prediction-interval width, individual stock…

AzioniDimensionamento delle posizioniCostruzione del portafoglioGestione del rischio
Machine Learning for Trading

This notebook compares Polymarket’s crypto-settled event contracts with the regulated Kalshi venue as sources of alternative data. It explains how access rules, settlement assets, position limits, and listing policies shape which participants influence…

CriptoSentimentStatisticaBasato su eventi
Machine Learning for Trading

This notebook explains an unconditional signature-based Wasserstein GAN for generating financial time series. It transforms returns into augmented paths, computes truncated path signatures, and trains an LSTM generator driven by Brownian noise to match…

Apprendimento automaticoStatisticaAzioniBacktest
Machine Learning for Trading

This dataset guide presents Binance perpetual futures price and volume data alongside an eight-hour premium index. Hourly OHLCV records describe market activity, while the premium measures the difference between perpetual and spot prices relative to spot. A…

CriptoFutures perpetuiMercati spotCarry
Machine Learning for Trading

This notebook evaluates stop losses, trailing stops, and fixed-duration exits on selected US equity strategies. A stop loss responds to losses from entry, a trailing stop responds to declines from a position’s peak, and a time exit closes after a set holding…

AzioniGestione del rischioBacktestDimensionamento delle posizioni
Machine Learning for Trading

This notebook demonstrates feature-drift checks on ETF momentum and technical features across calm and stressed windows, and on crypto perpetuals with premium-index data across market regimes. It compares Population Stability Index (PSI), including its…

Apprendimento automaticoStatisticaGestione del rischioCripto
Machine Learning for Trading

This notebook evaluates TSMixer as a global sequence model for an ETF panel. The model shares parameters across funds while using each fund’s own history and covariates; its mixing layers learn temporal and within-series relationships without combining one…

Apprendimento automaticoAzioniStatisticaBacktest
Machine Learning for Trading

This notebook defines forward-return targets for a cross-sectional futures strategy that ranks products by term structure, going long those with stronger carry and short those with weaker carry. It distinguishes roll-adjusted prices, appropriate for…

FuturesMaterie primeCarryStatistica
Machine Learning for Trading

This notebook explains how to interpret Kalshi’s federal funds rate contracts and prepare their prices for quantitative research. A binary contract price represents an implied event probability, but the feed contains the highest standing YES bid rather than…

Reddito fissoStatisticaMicrostruttura del mercatoArbitraggio
Machine Learning for Trading

This notebook describes using TSMixer to predict stock returns from ordered windows of each stock’s features. Each example contains 60 consecutive sessions; windows that cross gaps in a stock’s history are excluded, so the number of usable examples varies…

AzioniMercati statunitensiApprendimento automaticoBacktest
Machine Learning for Trading

This guide organizes US equity datasets into market data, company fundamentals, investor positioning, and firm characteristics. It inventories loaders for daily and intraday bars, options, and market microstructure records, as well as SEC filing text, XBRL…

AzioniMercati statunitensiMicrostruttura del mercatoOpzioni
Machine Learning for Trading

This notebook turns validation predictions from multiple model families into equal-weight, long-short portfolios. It ranks stocks by predicted return, buys the top names and shorts the bottom names, and sweeps several portfolio concentrations using a shared…

AzioniMercati statunitensiBacktestCostruzione del portafoglio
Machine Learning for Trading

This notebook compares equity and futures commission models alongside several slippage models, emphasizing that their units and assumptions differ. Percentage fees stay constant as a share of notional, while minimums, fixed charges, per-share fees, and tier…

EsecuzioneBacktestMicrostruttura del mercatoGestione del rischio
Machine Learning for Trading

This document describes training a Proximal Policy Optimization agent to liquidate a fixed order over a defined horizon. It evaluates the learned pacing policy against TWAP and an Almgren-Chriss schedule using the same simulated market paths, enabling paired…

CriptoEsecuzioneMicrostruttura del mercato
Machine Learning for Trading

This document presents a staged process for linking company names from filings, news, and alternative data to securities. It distinguishes entity identifiers such as CIK and LEI from security identifiers such as FIGI, CUSIP, and ISIN, and explains why…

AzioniApprendimento automaticoStatistica
Machine Learning for Trading

This document explains a supervised autoencoder factor model for predicting stock returns in an equity option analytics research setting. Unlike PCA, IPCA, and an unsupervised conditional autoencoder, its training objective combines reconstruction of the…

AzioniApprendimento automaticoInvestimento fattorialeStatistica
Machine Learning for Trading

This document describes a fixed out-of-sample backtest for a selected CME futures strategy. The configuration, predictions, allocator, rebalance cadence, concentration, and transaction-cost assumptions are inherited from earlier research steps and applied…

FuturesBacktestStatisticaGestione del rischio
Machine Learning for Trading

This notebook studies linear prediction models for a cross-section of CME futures products using feature columns grouped into related families, including carry, momentum, volatility, and rolling risk measures. Because columns within a family are often…

FuturesApprendimento automaticoInvestimento fattorialeStatistica
Machine Learning for Trading

This educational analysis compares four neural network designs on the same task: predicting the next daily return from a recent window of returns. A pooled set of eight exchange-traded funds provides varied market exposures. The models differ in how they…

AzioniApprendimento automaticoBacktestGestione del rischio
Machine Learning for Trading

This notebook audits the complete set of canonical validation predictions for an FX pairs study. It checks model identity, artifact availability, completeness, configured-label coverage, and whether the assembled configurations match the declared menu.…

ForexApprendimento automaticoStatisticaBacktest
Machine Learning for Trading

This notebook audits the registered validation predictions for an FX pairs study. It checks that the catalog includes the configured model population, complete prediction sets, available artifacts, and current model identities. It then summarizes predictive…

ForexApprendimento automaticoStatisticaBacktest
Machine Learning for Trading

This notebook measures how well the Lee-Ready method infers trade aggressor direction using Nasdaq order-by-order messages with venue-provided aggressor labels as ground truth. It reconstructs the limit order book from adds, modifications, cancellations,…

AzioniMicrostruttura del mercatoEsecuzioneStatistica