Tietokirjasto

The document demonstrates a VectorBT workflow for a long-only Bitcoin RSI mean-reversion rule. It calculates RSI from daily close prices, enters when the prior day’s reading falls below a lower threshold, and exits when it exceeds an upper threshold.…

This notebook builds N-BEATS for forecasting SPY closing prices. Fully connected blocks process a complete lookback window and each produces a backcast, which is subtracted before the next block, and a forecast for the future horizon. Constrained blocks use…

The document describes end-to-end pipelines for combining historical and recent equity data and preparing hourly crypto perpetual data. Its central example stitches WikiPrices and Yahoo observations at their coverage boundary, validates OHLC data and…

This notebook presents a structured state record for research agents that preserves a run's question, cutoff, evidence, unresolved questions, tool history, quality-gate results, and synthesis status. It distinguishes this state from a conversation trace: the…

This tutorial explains how to detect and prevent lookahead bias in ETF and macroeconomic data. It demonstrates that centered moving averages use future observations, while a simple correlation between a feature's level and next-period return can miss…

The document evaluates how trading costs change the validation performance of one previously selected S&P 500 equity and options allocation. It sweeps one-way charges as a fraction of traded value and compares them with a flat per-share commission and…

The notebook trains a vanilla autoencoder on standardized hourly returns for a group of crypto perpetual markets. Its encoder compresses the cross-asset return vector into a two-dimensional latent representation, and its decoder reconstructs the input. The…

This notebook uses correlation-based principal component analysis on sector ETF returns to describe common risk factors and sector rotation. Standardizing returns gives each sector unit variance before decomposition, reducing the chance that a more volatile…

The notebook compares Monte Carlo dropout and deep ensembles as ways to estimate uncertainty around daily ETF return forecasts. Dropout remains active across repeated predictions, while ensemble members are separately trained; the spread of predictions from…

This audit compares multiple backtesting engines on shared historical inputs for ETF allocation, CME futures, crypto perpetuals with funding, foreign exchange, and US equities. Each supported pair receives the same content-addressed market data and frozen…

This notebook surveys methods for detecting changes in a financial time series, using SPY as an example. It contrasts the Zivot–Andrews test, which estimates one break while testing a unit-root hypothesis, with PELT and binary segmentation, which partition a…

This notebook evaluates a pretrained financial sentiment model on FinMarBa headlines and explains why the result is not a clean test of sentiment transfer. FinBERT was trained against human judgments of text sentiment, while FinMarBa’s negative, neutral, and…

This notebook implements a long-only RSI mean-reversion rule for BTC/USDT perpetuals using an event-driven backtesting engine. It aggregates intraday bars into UTC daily OHLCV data, computes a rolling gain-and-loss RSI, enters when the indicator falls below…

This notebook explains a principal-component baseline for forecasting CME futures returns. PCA is fitted to the training fold’s product-return panel, so its components represent co-movement directions across contracts rather than compressed carry, momentum,…

This notebook compares weekly-grid sequence models for predicting the same five-session forward return used on a daily grid. Sampling on Fridays makes that label approximately a one-step-ahead target, allowing a direct regression formulation to be compared…

This notebook builds a retrieval-augmented research assistant over excerpts from US company 10-K filings. It loads filings, splits text into sentence windows, embeds the chunks with a local model, and indexes them for similarity search. The demonstrated run…

This document describes how to select a frozen set of equities for validation and holdout backtests using an estimated round-trip trading cost. The proxy combines estimated per-share costs relative to mean share price with twice the median half-spread, both…

This exploratory analysis profiles a daily panel of exchange-traded funds across nine groups. It checks symbol coverage through time, compares the panel against the classification dictionary, reviews nulls and zero-volume observations, and evaluates…

This notebook demonstrates training a generative adversarial network on daily ETF features with differential privacy. It introduces the privacy guarantee and budget, then uses Opacus to clip each sample's discriminator gradient before aggregating gradients…

This notebook applies TabM, a weight-sharing neural ensemble, to stock-return targets built from equity-option features. Its members use a common two-layer network backbone, with separate activation scaling vectors and output heads whose predictions are…

This notebook explains how to construct forward price-return labels for an eight-hourly crypto perpetuals panel. It shifts bar-open timestamps to the time completed-bar data becomes available, then calculates future returns on the contract price series…

This dataset note describes a diversified collection of exchange traded funds used in a momentum strategy and a broader sequence of financial research examples. It provides daily open, high, low, close, and volume observations beginning in 2006, grouped…

This chapter surveys ways to generate synthetic financial data and how to decide whether the output is useful. It starts from the limited number of realized market paths and the risk that repeated strategy searches inflate apparent backtest performance. It…

This case study compares declared fixed and trailing stop rules for FX positions. It first selects one parent strategy per label from a sealed validation cohort, then varies only the position risk rule. Because stops respond to price movements within a…