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Hae kirjastosta

1,124 dokumenttia

Machine Learning for Trading

This notebook adapts Diffusion-TS to generate synthetic daily ETF return sequences. Its denoiser predicts the original series and decomposes that prediction into a polynomial trend, selected Fourier components, and a residual. A combined time-domain and…

KoneoppiminenVolatiliteettiTilastotiedeUsean omaisuuslajin sijoittaminen
Machine Learning for Trading

This notebook builds a monitoring workflow for a deployed financial model using real holdout artifacts. It checks that incoming feature data is populated and structurally valid before measuring drift, then compares reference and current windows. Feature…

KoneoppiminenTilastotiedeRiskienhallintaOsakkeet
Machine Learning for Trading

This notebook introduces vectorized backtesting with VectorBT through a long-only RSI mean-reversion rule on Bitcoin perpetual market data. It calculates a close-based RSI, enters when the prior day’s reading is below a lower threshold, and exits when it is…

KryptovaratPerpetuaaliset futuuritPalautuminen keskiarvoonTekniset indikaattorit
Machine Learning for Trading

This notebook connects position sizing with analysis of maximum adverse and favorable excursions. It demonstrates fixed-fractional sizing, where shares are determined by a per-trade risk budget and entry-to-stop distance, subject to a concentration cap. It…

Position koon määrittäminenRiskienhallintaVolatiliteettiHistoriatestaus
Machine Learning for Trading

This notebook explains how to interpret Ridge regression forecasts for an ETF panel with SHAP. For a linear model, each feature’s attribution is its coefficient times the feature’s deviation from its training-background mean, so the attributions can be…

KoneoppiminenTilastotiedeOsakkeetHistoriatestaus
Machine Learning for Trading

This notebook compares two methods for estimating bid-ask spreads when historical data contains daily open, high, low, close, and volume but no quotes. Corwin-Schultz separates spread from volatility by comparing daily and two-day high-low ranges, relying on…

Markkinoiden mikrorakenneToimeksiantojen toteutusTilastotiedeRiskienhallinta
Machine Learning for Trading

This notebook presents a workflow for turning SEC annual and quarterly filings into structured text suitable for later sentiment, topic, and embedding analysis. It maps desired sections to form-specific item numbers, emphasizing that management discussion…

OsakkeetMarkkinatunnelmaTapahtumapohjainen kaupankäyntiTilastotiede
Machine Learning for Trading

This notebook explains how a conditional autoencoder extends an instrumented principal component model: a neural network maps stock characteristics to factor exposures, while the model retains the same exposure-times-factor-return structure. It fits the…

OsakkeetFaktoripohjainen sijoittaminenKoneoppiminenHistoriatestaus
Machine Learning for Trading

This study screens financial and GJR-GARCH volatility features against forward equity returns. For each session, it calculates the rank correlation across stocks between a feature and subsequent returns, then assesses average association, consistency across…

OsakkeetOptiotTilastotiedeKoneoppiminen
Machine Learning for Trading

This reference describes two financial news corpora used for sentiment analysis, text-feature development, and experiments linking news to returns. FNSPID connects headlines with stock tickers and covers a broad historical span; a smaller sample is available…

OsakkeetMarkkinatunnelmaKoneoppiminenTilastotiede
Machine Learning for Trading

The document derives European call and put prices under Black-Scholes, checks their relationship through put-call parity, and computes implied volatility by numerically solving for the volatility that matches an observed option price. It also defines Delta,…

OptiotJohdannaisten hinnoitteluVolatiliteettiRiskienhallinta
Machine Learning for Trading

This notebook compares six allocation methods on crypto perpetual strategies that have already passed a baseline selection funnel. It keeps each strategy’s predictions and entry rules fixed, then changes how capital is assigned: one allocator uses model…

KryptovaratPerpetuaaliset futuuritPosition koon määrittäminenSalkun muodostaminen
Machine Learning for Trading

This notebook demonstrates deep hedging for a short European call. It simulates underlying price paths with geometric Brownian motion, calculates Black-Scholes delta as a benchmark, and trains a semi-recurrent neural network to choose hedge positions. The…

OptiotKoneoppiminenRiskienhallintaJohdannaisten hinnoittelu
Machine Learning for Trading

This module defines how a holdout selection field is assembled across validation stages and strategy labels. It addresses discrepancies that can make frozen candidate sets and live reconstruction select different configurations, or cause later analysis to…

HistoriatestausRiskienhallintaSalkun muodostaminenTilastotiede
Machine Learning for Trading

This feature-engineering module prepares daily price data for systematic macro portfolio models. It creates horizon returns scaled by estimated volatility, several multi-scale MACD signals, and rolling z-scores of log prices. The return horizons can use a…

Usean omaisuuslajin sijoittaminenHintamomentumTrendin seuraaminenVolatiliteetti
Machine Learning for Trading

This notebook shows how to test whether four managed-portfolio strategies add explanatory power for SPY returns after controlling for broad movements captured by ten principal components. The candidate portfolios rank ETFs by recent momentum, low volatility,…

OsakkeetKoneoppiminenTilastotiedeRiskienhallinta
Machine Learning for Trading

This document describes a workflow for turning quarterly SEC 13F bulk filings into an institutional holdings panel. It selects the latest filing per manager by filing date, uses CUSIP rather than filer-entered company names to identify securities, and…

OsakkeetYhdysvaltain markkinatTilastotiedeMarkkinatunnelma
Machine Learning for Trading

The document explains instrumented principal component analysis for modeling ETF returns. Unlike ordinary PCA, which assigns each fund fixed factor exposures, IPCA makes each fund’s exposures a shared linear function of its observable features. It estimates…

OsakkeetFaktoripohjainen sijoittaminenKoneoppiminenTilastotiede
Machine Learning for Trading

This notebook uses NASDAQ ITCH trade records to examine how volume and trading activity vary through the regular session. It resamples trades into time bars, compares price and volume for representative high-, medium-, and low-activity stocks, then…

OsakkeetMarkkinoiden mikrorakenneTilastotiedeTekniset indikaattorit
Machine Learning for Trading

This notebook compares predictive models for ranking NASDAQ-100 stocks by their next 15-minute return using intraday microstructure information such as spreads, depth imbalance, signed volume, and price impact. It selects one representative prediction set…

OsakkeetKoneoppiminenTilastotiedeMarkkinoiden mikrorakenne
Machine Learning for Trading

This notebook derives Kelly sizing for a binary wager by maximizing expected log wealth, then illustrates how stake size changes the distribution of simulated wealth paths. It extends the framework to continuous returns and multi-asset portfolios, where the…

Position koon määrittäminenRiskienhallintaSalkun muodostaminenTilastotiede
Machine Learning for Trading

This evaluation screens option-surface, price-derived, and conditional-volatility features against forward equity returns. For each session it computes a cross-sectional rank correlation between feature values and subsequent returns, then examines the time…

OsakkeetOptiotTilastotiedeKoneoppiminen
Machine Learning for Trading

This notebook evaluates a previously selected ETF strategy on a reserved holdout period. It retrieves the matching predictions from their training identity and checkpoint, applies the strategy specification and trading costs established earlier, and…

HistoriatestausSalkun muodostaminenRiskienhallintaPosition koon määrittäminen
Machine Learning for Trading

This module explains how to prepare historical S&P 500 prices when ticker symbols can refer to different securities over time. It applies corporate-action adjustment factors, tracks security identifiers, and marks identity boundaries. Return calculations are…

OsakkeetYhdysvaltain markkinatHistoriatestausToimeksiantojen toteutus