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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
Quantpedia
86 documents
TqSdk
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

511 documents

QuantInsti blog

The document introduces general and finance-tuned language models, then describes using natural language processing to turn financial text into sentiment measures. It outlines a workflow for collecting and preprocessing Federal Open Market Committee…

SentimentMachine learningUS marketsEvent-driven
QuantInsti blog

The article introduces algorithmic trading as using coded rules to generate and execute orders, then compares it with manual trading. It highlights speed, simultaneous monitoring of markets, reduced reliance on emotional judgment, and the ability to backtest…

ExecutionBacktestingRisk managementHigh-frequency trading
QuantInsti blog

This project describes two classifiers intended to predict whether Bank Nifty and its leading constituents would open higher or lower on the following trading day. The stock models use daily OHLCV history and technical indicators for five constituents; the…

Machine learningEquitiesTechnical indicatorsBacktesting
QuantInsti blog

The article explains data engineering as the work of collecting, preparing, organizing, and maintaining data so analysts and trading models can use it reliably. It describes engineers as building data infrastructure and pipelines, removing problems such as…

Machine learningBacktestingRisk managementStatistics
QuantInsti blog

The article considers how increasingly capable artificial intelligence could change trading and financial markets. It distinguishes current rule-based automated trading from systems that learn and adapt, then speculates that AI could assess technical,…

Machine learningEquitiesMarket microstructureRisk management
QuantInsti blog

This webinar description explains how high-frequency prices can extend portfolio risk analysis beyond the low-frequency data commonly used in portfolio metrics. The proposed approach uses intraday observations to estimate risk and support portfolio…

EquitiesStatisticsRisk managementPortfolio construction
QuantInsti blog

This event overview outlines a two-day NSE workshop on algorithmic trading, with material spanning strategy research, trading technology, regulation, and portfolio management. Topics include execution methods such as time- and volume-weighted orders,…

ExecutionMarket microstructureHigh-frequency tradingRisk management
QuantInsti blog

This interview describes David U. Ordiz’s progression from discretionary Bund futures trading to systematic research and portfolio management. His approach focuses on intraday algorithms seeking short-term trend or counter-trend moves across index futures,…

FuturesVolatilityRisk managementBacktesting
QuantInsti blog

The article introduces multithreading as a way to handle several stock data downloads concurrently. Since network requests spend time waiting for external responses, separate threads can work on different tickers while other requests are pending. It outlines…

EquitiesExecutionBacktesting
QuantInsti blog

The article explains Linear Discriminant Analysis (LDA) as a supervised method for classifying observations and estimating the probability of belonging to a class. It contrasts LDA with logistic regression and describes LDA’s use of Bayes’ theorem, class…

Machine learningRisk managementPairs tradingPortfolio construction
QuantInsti blog

This article introduces FIX as a standardized messaging protocol used to connect participants and systems across electronic trading workflows. It describes how a shared format can reduce integration effort, simplify communication with multiple brokers, and…

ExecutionMarket microstructureHigh-frequency trading
QuantInsti blog

This article uses simple betting examples to explain expected value as the probability-weighted average of gains and losses. It shows how a favorable payoff structure can produce positive expectation even when a win is uncertain, while a symmetric…

StatisticsRisk managementPortfolio constructionOptions
QuantInsti blog

This document explains ADDM, a method for detecting changes in a trading model’s prediction errors and adapting the model when market conditions shift. Its detector uses a Self-Exciting Threshold Autoregressive (SETAR) model to divide error behavior into…

Machine learningStatisticsBacktesting
QuantInsti blog

This interview with trader Priyanka S. includes practical advice for developing and testing equity signals. She cautions that familiar technical indicators such as moving average crossovers may contain little information about future prices, and encourages…

EquitiesTechnical indicatorsBacktestingFactor investing
QuantInsti blog

The article explains how a time-series generative adversarial network can produce synthetic financial observations when historical data is limited. It describes the generator and discriminator conceptually, then focuses on the conditional probabilistic…

Machine learningBacktestingEquitiesStatistics
QuantInsti blog

This project tests a simple ETF pairs strategy in oil, technology, and financial sectors: USO with XLE, XLK with IYW, and XLF with PSCF. It estimates a hedge ratio by regression, evaluates spread stationarity with an Augmented Dickey-Fuller test, then enters…

EquitiesPairs tradingMean reversionArbitrage
QuantInsti blog

The document explains how to stitch successive futures contracts into a longer time series for analysis when each individual contract has limited history. Simply joining contract prices can create artificial jumps because adjacent expiries may trade at…

FuturesCommoditiesBacktestingStatistics
QuantInsti blog

The article introduces spread trading as a hedged position that buys and sells related contracts, such as options on the same security with different strikes or expiries, or futures with different delivery months, commodities, or locations. It recommends…

OptionsFuturesCommoditiesRisk management
QuantInsti blog

The article demonstrates simple and multiple linear regression on historical returns for Coca-Cola, PepsiCo, the S&P 500 ETF, and the US Dollar Index. It first uses pairwise correlations, then fits a single-predictor model for Coca-Cola returns using the S&P…

EquitiesStatisticsMachine learningBacktesting
QuantInsti blog

The article introduces the Kalman filter as a recursive method for estimating a changing, partly unobserved state by combining model predictions with noisy measurements and their uncertainty. It explains concepts including normal distributions, variance,…

StatisticsPairs tradingVolatilityPortfolio construction
QuantInsti blog

The article explains divergence as a mismatch between an asset’s price swings and an indicator or oscillator’s swings. It distinguishes regular bullish and bearish divergence, which may warn of a trend reversal, from hidden bullish and bearish divergence,…

Technical indicatorsTrend followingMean reversionRisk management
QuantInsti blog

This overview explains high-frequency trading as automated order placement that depends on rapid market data, fast decision systems, and low-latency execution. It describes co-location, tick-by-tick feeds, and market making, where firms quote both sides and…

High-frequency tradingMarket makingMarket microstructureVolatility
QuantInsti blog

This guide describes a walk-forward workflow for forecasting stock prices with XGBoost. It motivates repeated model updates as a response to concept drift and changing data distributions. Historical price data are cleaned, adjusted prices are used, and…

Machine learningEquitiesBacktestingTechnical indicators
QuantInsti blog

This project tests a market-neutral pairs strategy on Brazilian equities, grouping stocks by sector and screening pairs with the Johansen cointegration test. It keeps pairs with a consistently signed spread and a half-life no longer than 60 days. Entry and…

EquitiesPairs tradingMean reversionStatistics