Tudástár

This case study explains why stop-losses, trailing stops, and other position-level overlays cannot be tested for US firm-characteristics portfolios built from monthly observations. The vectorized backtest applies one weight vector to a realized forward…

This notebook establishes a signal-stage baseline for evaluating model predictions on CME futures. At each weekly decision, it ranks products by predicted return, buys the top group, shorts the bottom group, and gives each position equal weight. It varies…

This notebook uses linear regression as a baseline for forecasting NASDAQ-100 returns from minute-level microstructure features. Because spread and order-imbalance measures overlap across lookbacks, the feature matrix is highly collinear: ordinary least…

This notebook compares two causal questions about extreme premiums in Bitcoin perpetual futures: whether a premium state predicts forward returns, and whether it predicts subsequent premium reversion. The treatment is a binary indicator for a premium z-score…

This notebook presents a workflow for deciding how financial time series should be modelled. It begins with plots of SPY prices and returns, VIX levels, and the return distribution to identify drifting levels, clustered moves, and heavy tails. It then…

This configuration specifies a long-only ETF ranking and rebalancing experiment. It defines a broad 100-ETF universe spanning equity, bond, commodity, and regional exposures, with monthly decisions and next-open execution. The main label forecasts a 21-day…

This notebook builds prediction intervals around gradient boosting forecasts and compares split conformal prediction with quantile regression, conformalized quantile regression, and adaptive conformal inference. Split conformal uses a chronological training…

This US equities panel study uses a penalized linear model as a baseline for forecasting forward returns from features observed at the close. Its feature set contains overlapping momentum and volatility measurements, creating multicollinearity that can make…

This benchmark compares CSV, Parquet, Feather (Arrow IPC), and HDF5 using the same deterministic OHLCV panel. It measures write time, materialized read time, file size, and the effect of reading only selected columns. The timing policy uses a single write…

This ETF feature study builds three model-derived inputs: a hidden Markov model (HMM) that estimates market regime, fractionally differenced prices for reference funds, and per-ETF GARCH(1,1) conditional volatility. It emphasizes that avoiding look-ahead…

This notebook explains instrumented principal component analysis (IPCA), which estimates common factors while making each stock’s factor exposures a shared linear function of its same-day option-surface features. Those features include implied volatility,…

This tutorial compares fixed stop-loss and take-profit barriers, trailing stops, ATR-scaled exits, a machine-learning exit signal, and combinations of these approaches on daily SPY data. Fixed barriers are simulated from entry prices, with overnight gaps…

This study compares gradient-boosted trees with a penalized linear model for ranking currency pairs by future returns. Its motivation is that trees can represent conditional relationships, such as using momentum in one market regime and carry in another,…

This exploratory analysis profiles a four-hour OANDA dataset covering twenty currency pairs. It explains that the volume field counts quote updates at one venue, not consolidated traded size, and therefore cannot support a market-wide liquidity ranking. It…

This operational tutorial demonstrates runtime safeguards for a live trading system using a synthetic broker. It shows how SafeBroker rejects orders when market data is too old or daily losses exceed a configured limit, and how the loss-triggered kill switch…

This notebook assembles a forecasting agent from a language-model client, a search tool, and a bounded turn loop. The agent searches for evidence and returns a binary probability with a rationale. Its response parser handles common formatting failures and…

This notebook applies Bayesian structural time-series event-study methods to estimate the impact of Federal Reserve announcements on a bond ETF. It builds a counterfactual from pre-event relationships between the target’s daily log returns and returns on…

This notebook compares DQN, PPO, and A2C in a shared simulated trading environment calibrated to hourly Bitcoin perpetual-futures returns. A GARCH(1,1) model supplies volatility clustering, while the environment charges for position changes and applies an…

This notebook tests whether four managed-portfolio strategies explain SPY returns after controlling for broad ETF co-movement. The strategies rank ETFs using lagged momentum, volatility, or recent returns. Ten principal components from a balanced set of…

This notebook adapts Word2Vec skip-gram to institutional equity holdings. It treats each institution’s portfolio as a sentence, stock identifiers as tokens, and positions ordered by reported holding value as word order. Stocks that repeatedly appear in…

This notebook explains why raw equity prices can produce misleading returns when they cross stock splits or cash dividend dates. It describes backward adjustment, which scales historical prices for later corporate actions, and distinguishes raw prices for…

This notebook tests whether model-based position sizing improves on equal weighting in a US firm characteristics strategy. It compares score weighting, which allocates more to names with higher predicted scores, with conformal weighting, which reduces…

This notebook evaluates whether a signal’s information coefficient (IC) is distinguishable from noise when successive observations are dependent. It illustrates the issue using a momentum signal and ETF data, explaining how overlapping forward returns,…

This notebook compares ways to exit long ETF trades: fixed profit targets and stops, trailing stops, ATR-scaled barriers, machine-learning signals, and combinations. It builds indicators and trade simulations, then summarizes trade returns, win rates,…