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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
Binance API docs
45 documents
Quantopian lectures
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
WonderTrader
14 documents
Alphalens
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

4,613 documents

Machine Learning for Trading

The document demonstrates a broker wrapper that checks orders and portfolio state before forwarding trades. Its controls include per-order share and value caps, position exposure limits, order-rate limits, asset allow and block lists, and an emergency halt.…

Risk managementPosition sizingExecutionBacktesting
Machine Learning for Trading

This notebook turns 21-day forward ETF returns into an up-or-down target and evaluates L1- and L2-regularized logistic regression on chronological walk-forward folds. It scales features using each fold’s training data, then assesses predictions with…

Machine learningStatisticsPosition sizingEquities
Machine Learning for Trading

This notebook investigates why two backtesting engines can produce different results from identical signals. It holds a precomputed ETF strategy fixed, forms a deterministic portfolio from its predictions, and changes execution settings one at a time. The…

BacktestingExecutionMarket microstructurePosition sizing
Machine Learning for Trading

This notebook explains how market impact grows with order size and uses the square-root participation model to frame strategy capacity. It distinguishes inputs available from market data, such as volatility and average daily volume, from the impact…

Market microstructureExecutionRisk managementPosition sizing
Machine Learning for Trading

This notebook examines how return forecasts for ETFs can express uncertainty, comparing Monte Carlo dropout with deep ensembles. Dropout remains active across repeated inference passes, while an ensemble measures disagreement among separately trained…

EquitiesMachine learningStatisticsRisk management
Machine Learning for Trading

This notebook connects position sizing with analysis of maximum adverse and favorable excursions. It demonstrates fixed-fractional sizing, where shares are determined by a per-trade risk budget and entry-to-stop distance, subject to a concentration cap. It…

Position sizingRisk managementVolatilityBacktesting
Machine Learning for Trading

This notebook compares six allocation methods on crypto perpetual strategies that have already passed a baseline selection funnel. It keeps each strategy’s predictions and entry rules fixed, then changes how capital is assigned: one allocator uses model…

CryptoPerpetual futuresPosition sizingPortfolio construction
Machine Learning for Trading

This notebook derives Kelly sizing for a binary wager by maximizing expected log wealth, then illustrates how stake size changes the distribution of simulated wealth paths. It extends the framework to continuous returns and multi-asset portfolios, where the…

Position sizingRisk managementPortfolio constructionStatistics
Machine Learning for Trading

This notebook evaluates a previously selected ETF strategy on a reserved holdout period. It retrieves the matching predictions from their training identity and checkpoint, applies the strategy specification and trading costs established earlier, and…

BacktestingPortfolio constructionRisk managementPosition sizing
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 case study compares alternative position sizing methods for CME futures against an equal weight baseline. It explains how inverse volatility sizing uses each contract’s own volatility, while covariance based methods also account for relationships…

FuturesPosition sizingPortfolio constructionRisk management
Machine Learning for Trading

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…

Machine learningStatisticsRisk managementPosition sizing
Machine Learning for Trading

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…

EquitiesPosition sizingPortfolio constructionBacktesting
Machine Learning for Trading

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,…

EquitiesRisk managementPosition sizingVolatility
Machine Learning for Trading

This notebook introduces futures backtesting mechanics through a long-short momentum example across several asset classes. It explains how contract specifications convert price movements into dollar profit and loss through multipliers, and how notional…

FuturesMomentumBacktestingPosition sizing
Machine Learning for Trading

This notebook evaluates position-level stop losses, trailing stops, and time exits on the allocation lineage that ranks highest by validation Sharpe among eligible full-coverage candidates. Each overlay is compared with its own no-overlay parent, isolating…

EquitiesOptionsRisk managementBacktesting
Machine Learning for Trading

This notebook compares portfolio weighting rules for an S&P 500 options strategy that sells straddles. It holds the selected symbols and other strategy settings fixed while replacing the equal-weight baseline with allocators that use either model predictions…

OptionsEquitiesPortfolio constructionPosition sizing
Machine Learning for Trading

This chapter presents risk management as part of strategy design and live operations. It covers tail risk measurement with value at risk and conditional value at risk, drawdown depth and recovery, exposure decomposition, stress testing, adaptive controls,…

Risk managementVolatilityBacktestingPortfolio construction
Machine Learning for Trading

This notebook shows how to tune stop-loss, take-profit, and trailing-stop rules for a momentum portfolio while preserving a chronological holdout. It compares one-dimensional parameter sweeps, joint rule grids, and stop or target priors derived from…

Risk managementBacktestingPosition sizingMomentum
Machine Learning for Trading

This shared allocation module turns cross-sectional predictions into portfolio weights. It ranks assets by score to select long positions or top and bottom baskets for long-short portfolios. Position weights can reflect conformal prediction widths, where…

Portfolio constructionPosition sizingRisk managementStatistics
Machine Learning for Trading

This notebook compares portfolio sizing methods for leading S&P 500 equity signal configurations. It first selects each configuration’s strongest full-coverage checkpoint using validation Sharpe, then applies alternative allocators—including score weighting,…

EquitiesPortfolio constructionPosition sizingBacktesting
Machine Learning for Trading

The document explains three rules for closing positions before a portfolio’s scheduled rebalance: a fixed loss stop, a trailing stop measured from the position’s best price, and a time exit that closes after a set holding period. Because overlays only remove…

EquitiesRisk managementBacktestingPosition sizing
Machine Learning for Trading

This notebook presents a workflow for asking whether six-month skip-recent momentum affects 21-day forward returns and whether the effect changes with market volatility. It estimates effects on training data using double machine learning, with volatility and…

EquitiesMomentumMachine learningStatistics
Machine Learning for Trading

This notebook describes how to evaluate a previously selected FX strategy on holdout predictions. It reuses the selected configuration’s registered backtest specification, keeping the signal, allocation, risk controls, rebalance rule, costs, and account…

ForexBacktestingRisk managementPosition sizing