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Knowledge library

Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.

Quant Q&A
20,364 documents
SuperMind
12,226 documents
OKX Learn
8,431 documents
Strategy library
7,910 documents
MQL5 code base
7,090 documents
BigQuant
3,481 documents
Bitget Academy
3,298 documents
MQL5 articles
3,012 documents
TradingView scripts
1,976 documents
ProRealCode
1,507 documents
Deribit Insights
1,232 documents
Machine Learning for Trading
1,124 documents
arXiv papers
1,033 documents
Amberdata research
766 documents
FMZ forum
682 documents
FMZ digest
662 documents
vn.py community
560 documents
QuantInsti blog
511 documents
Galaxy Research
340 documents
QuantStart
246 documents
Stratmill research code
219 documents
Robot Wealth
195 documents
NautilusTrader
191 documents
Hummingbot docs
181 documents
Paradigm research
175 documents
Lumibot
164 documents
Kraken Learn
163 documents
Quant course library
157 documents
OctoBot
152 documents
Cryptohopper blog
144 documents
Systematic trading blog (Rob Carver)
132 documents
Qlib
116 documents
TqSdk
86 documents
Quantpedia
86 documents
Hyperliquid docs
79 documents
Freqtrade
68 documents
Hudson & Thames
62 documents
Awesome Systematic Trading
61 documents
backtrader
54 documents
vn.py
50 documents
Quantopian lectures
45 documents
Binance API docs
45 documents
FMZ guides
38 documents
pysystemtrade
34 documents
Freqtrade docs
32 documents
quant-trading
31 documents
FinRL
28 documents
Zipline
22 documents
FMZ live strategies
21 documents
Jesse
17 documents
pyfolio
16 documents
Alphalens
14 documents
WonderTrader
14 documents
backtesting.py
11 documents
Technical Analysis
9 documents
QTPyLib
8 documents
QuantRocket
7 documents
Lumibot strategies
7 documents
Awesome Quant
1 documents

Search the library

1,124 documents

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…

BacktestingStatistics
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…

Machine learningStatisticsBacktestingRisk management
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…

Technical indicatorsMomentumVolatilityMachine learning
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…

OptionsExecutionBacktestingRisk management
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…

Risk managementExecutionBacktesting
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…

EquitiesOptionsFactor investingMachine learning
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…

BacktestingRisk managementStatistics
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…

EquitiesMachine learningStatisticsRisk management
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…

EquitiesMarket microstructureExecution
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…

BacktestingMachine learningStatistics
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…

OptionsRisk managementBacktestingDerivatives pricing
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…

Machine learningStatisticsSentimentEquities
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…

FuturesCommoditiesMachine learningBacktesting
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…

EquitiesVolatilityMarket microstructureStatistics
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…

CryptoPerpetual futuresMachine learningBacktesting
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…

Machine learningStatisticsEquitiesUS markets
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…

Machine learningStatisticsRisk management
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.…

EquitiesMachine learningMomentumMean reversion
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…

CryptoPerpetual futuresVolatilityMarket microstructure
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…

Machine learningStatisticsBacktesting
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…

Machine learningExecutionMarket microstructureVolatility
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…

EquitiesMachine learningStatisticsBacktesting
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…

ForexRisk managementBacktestingPosition sizing
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…

EquitiesMachine learningStatisticsBacktesting