Library ng kaalaman

This notebook describes how to create holdout predictions for an FX strategy after its configuration has already been selected using validation results. It resolves the highest-Sharpe validation backtest among admitted candidates that remained solvent, then…

This notebook explains a market data lifecycle for daily OHLCV data: load an initial history, fetch a small overlapping delta, detect missing sessions, and summarize each symbol’s freshness and validation issues. It describes why the update window should…

This notebook compares DQN, PPO, and A2C in a simulated cryptocurrency trading environment. It calibrates a GARCH(1,1) volatility process on hourly Bitcoin perpetual-futures returns, then simulates paths that preserve volatility clustering while leaving…

This notebook diagnoses how a fixed, long-only ETF momentum baseline performed across market conditions from 2010 to 2024. It labels each daily return using volatility and trend measures known before that return began, with expanding historical medians…

This notebook applies previously selected firm-characteristic predictions and a fixed portfolio configuration to a one-year holdout period. It makes no new choices about predictions, allocation, concentration, rebalancing, or transaction costs, preserving…

This notebook builds a forecasting agent that alternates between web searches and probability forecasts. A small two-method language-model interface lets the same loop use a mock, local, or commercial provider. The agent receives a prediction-market question…

This notebook examines information-driven sampling by constructing tick and volume imbalance bars from trade data. It verifies a manual tick-imbalance implementation against a library sampler, then sweeps expected bar sizes and adaptation settings to compare…

This audit compares trading engines replaying the same frozen model targets and content-addressed historical inputs across ETF allocation, futures, crypto perpetual funding, foreign exchange, and US equities. It evaluates execution parity through matching…

This notebook demonstrates a read-only question-answering workflow over an institutional holdings graph built from 13F filings. Instead of generating database queries from natural language, it routes exact supported questions to hand-written Cypher templates…

This analysis compares risk overlays across case studies by loading their overlay backtests from a registry and measuring Sharpe and maximum drawdown against an allocation-stage baseline, with a signal-stage fallback. It classifies configurations into rule…

This notebook evaluates position-level stop-losses, trailing stops, and time exits on top allocation-stage configurations in a NASDAQ-100 intraday strategy. Decisions occur every fifteen minutes while the backtest engine monitors positions every minute, so…

This notebook develops Black-Scholes pricing for European calls and puts, checks put-call parity, and computes implied volatility with Brent root-finding. It also defines Delta, Gamma, Vega, Theta, and Rho and uses plots to illustrate how option…

This notebook describes a patched transformer for sequence prediction in an S&P 500 equity and options study. It divides a time window into contiguous blocks and represents each block as a token, allowing attention to compare distant parts of the window…

This notebook explains instrumented principal component analysis (IPCA) for a changing US equities panel. Traditional PCA assigns each stock a fitted loading, which cannot naturally cover names absent from the training sample and stays fixed as a company's…

This chapter frames reinforcement learning as a tool for sequential control, distinguishing it from supervised forecasting. It focuses on tasks where actions affect later outcomes and rewards are comparatively concrete: trade execution, market making, and…

This ETF feature notebook explains how fitted statistical models can leak future information through their estimated parameters, even when their formulas use past observations. Its remedy is a walk-forward refit schedule: allow an initial burn-in, fit using…

This notebook reconstructs the selected FX pairs configuration from a frozen validation candidate set, then assesses its validation and holdout evidence. Selection uses the highest eligible validation Sharpe, with a deterministic backtest identity…

This notebook studies gradient boosting for a US equities panel, varying tree capacity, objective function, and training checkpoint across three forward-return horizons. It explains how shallow trees can learn feature interactions, while increasing capacity…

This notebook demonstrates SHAP explanations for a LightGBM model predicting ETF forward returns. TreeSHAP supplies feature contributions for individual predictions and aggregate importance summaries. Beeswarm and dependence plots show how feature values…

This notebook describes how to generate holdout predictions for a crypto perpetual funding strategy after its configuration has been selected using validation results. It resolves the highest-Sharpe eligible validation backtest, carries over the model…

This module defines a point-in-time simulation environment for executing a large sell order in crypto perpetual futures. Its observations combine remaining inventory and time with market volatility, premium index, relative volume, hour of day, and time to…

This chapter presents market data as the result of trading rules, liquidity, and participant behavior. It surveys data from top-of-book quotes through order-level feeds, then describes parsing exchange messages and replaying them into a venue-local limit…

This shared utility module supports causal analysis of trading data with time-series double machine learning (DML). Its documented tools include walk-forward estimation with an embargo, nuisance models, and refutation checks. Block permutation preserves…

This notebook explains TSMixer, a time-series neural network that alternates two operations: a shared linear map across lookback days for each feature, and a shared feature MLP applied independently at each day. Residual connections and pre-normalization…