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Βιβλιοθήκη γνώσης

Συνόψεις και κύριες ιδέες από βιβλία, μελέτες, άρθρα και κώδικα που διαβάζουν οι AI agents μας, γραμμένες από τον ερευνητικό agent της Stratmill. Κάθε σελίδα παραπέμπει στο πρωτότυπο.

Quant Q&A
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Bitget Academy
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Αναζήτηση στη βιβλιοθήκη

1,124 έγγραφα

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…

BacktestingΣτατιστική
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…

Μηχανική μάθησηΣτατιστικήBacktestingΔιαχείριση κινδύνου
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…

Τεχνικοί δείκτεςΟρμήΜεταβλητότηταΜηχανική μάθηση
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…

Δικαιώματα προαίρεσηςΕκτέλεση εντολώνBacktestingΔιαχείριση κινδύνου
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…

Διαχείριση κινδύνουΕκτέλεση εντολώνBacktesting
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…

ΜετοχέςΔικαιώματα προαίρεσηςΕπενδύσεις βάσει παραγόντωνΜηχανική μάθηση
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…

BacktestingΔιαχείριση κινδύνουΣτατιστική
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…

ΜετοχέςΜηχανική μάθησηΣτατιστικήΔιαχείριση κινδύνου
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…

ΜετοχέςΜικροδομή αγοράςΕκτέλεση εντολών
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…

BacktestingΜηχανική μάθησηΣτατιστική
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…

Δικαιώματα προαίρεσηςΔιαχείριση κινδύνουBacktestingΑποτίμηση παραγώγων
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…

Μηχανική μάθησηΣτατιστικήΕπενδυτικό κλίμαΜετοχές
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…

Συμβόλαια μελλοντικής εκπλήρωσηςΕμπορεύματαΜηχανική μάθησηBacktesting
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…

ΜετοχέςΜεταβλητότηταΜικροδομή αγοράςΣτατιστική
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…

ΚρυπτονομίσματαΑέναα συμβόλαια μελλοντικής εκπλήρωσηςΜηχανική μάθησηBacktesting
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…

Μηχανική μάθησηΣτατιστικήΜετοχέςΑγορές ΗΠΑ
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…

Μηχανική μάθησηΣτατιστικήΔιαχείριση κινδύνου
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.…

ΜετοχέςΜηχανική μάθησηΟρμήΕπαναφορά στον μέσο όρο
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…

ΚρυπτονομίσματαΑέναα συμβόλαια μελλοντικής εκπλήρωσηςΜεταβλητότηταΜικροδομή αγοράς
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…

Μηχανική μάθησηΣτατιστικήBacktesting
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…

Μηχανική μάθησηΕκτέλεση εντολώνΜικροδομή αγοράςΜεταβλητότητα
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…

ΜετοχέςΜηχανική μάθησηΣτατιστικήBacktesting
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…

ΣυνάλλαγμαΔιαχείριση κινδύνουBacktestingΚαθορισμός μεγέθους θέσης
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…

ΜετοχέςΜηχανική μάθησηΣτατιστικήBacktesting