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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
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

3,481 documents

BigQuant

This note reports results for five related quantitative factors. The starting signal standardizes the ratio of trading volume to turnover. Variants combine it with 20-period close-price variability, a momentum-style calculation based on recent price changes,…

Factor investingStatisticsBacktesting
BigQuant

The document summarizes a report comparing the historical performance and future prospects of index funds and actively managed funds. Its stated takeaway is that their past performance showed no clear overall winner. A slight historical advantage for active…

EquitiesFactor investingPortfolio construction
BigQuant

The document poses a model validation question rather than giving a complete answer: after training a multi-factor model on historical data and evaluating it on a later year, should a trader deploy that fitted model or retrain it on a newer rolling window?…

Machine learningFactor investingBacktestingStatistics
BigQuant

The document outlines a framework that combines monetary and credit conditions to time Chinese equities and bonds, then adds momentum, oil, or inflation signals for commodities, equities, and bonds. It argues that monetary policy can lead markets because of…

Multi-assetFactor investingMomentumPortfolio construction
BigQuant

The document explains softmax as a transformation from a vector of scores to nonnegative values that sum to one, making them interpretable as a probability distribution. It describes its role in the continuous bag-of-words model in Word2Vec, where model…

Machine learningStatistics
BigQuant

This literature summary reviews evidence that stocks with greater left-tail risk, measured using value at risk or expected shortfall, subsequently earn lower returns. The underlying study sorts stocks by tail-risk measures, compares portfolio returns and…

EquitiesRisk managementMomentumStatistics
BigQuant

This research summary examines whether detrended cumulative equity factor returns show cyclical behavior and whether macroeconomic cycles can help explain it. The proposed framework represents three cycles associated with Kitchin, Juglar, and Kuznets…

Factor investingStatisticsBacktesting
BigQuant

This opinion article argues that many retail investors lose through overtrading, chasing short-term leaders, and expecting rapid gains. It recommends accepting limits in prediction and focusing instead on patiently waiting for a market, sector, or stock to…

EquitiesTrend followingRisk managementChina markets
BigQuant

The document summarizes research applying Wasserstein generative adversarial networks (WGANs) to financial return series and comparing them with standard GANs. WGANs replace the original GAN’s distribution-distance measure with Wasserstein distance, which…

Machine learningStatisticsEquitiesChina markets
BigQuant

This platform-support exchange concerns a pair-trading strategy that failed to run in simulation. The suggested fix is to leave the data-extraction module’s start-date option unbound to live-trading parameters. The explanation is that the strategy’s…

Pairs tradingStatisticsBacktesting
BigQuant

This Chinese-language post discusses a small-cap equity strategy whose apparent style changed from small-cap exposure to liquidity exposure over a short period. It argues that factor premia are time-varying and that a strategy labeled as small-cap may earn…

China marketsEquitiesFactor investingStatistics
BigQuant

The article challenges the claim that Chinese stock index futures expiry reliably causes equity market declines. It argues that these contracts settle in cash, so settlement itself does not require buying or selling ETFs or underlying shares. Hedgers can…

FuturesEquitiesStatisticsSentiment
BigQuant

The document examines why ESG ratings from different providers can disagree, citing differences in factor definitions, data sources, weighting, and treatment of missing disclosures. It warns that ratings may reward companies with greater reporting resources…

EquitiesFactor investingStatisticsRisk management
BigQuant

This report surveys gold’s supply and demand, major trading venues, and common investment instruments. It notes that investment demand and central bank purchases are especially relevant to prices, and describes the roles of London spot trading, U.S. futures,…

CommoditiesFuturesSentimentBacktesting
BigQuant

This document describes an A-share strategy that rotates among industries according to a market-regime indicator, then selects stocks within chosen industries using four factors emphasizing short-term reversal and low attention. The regime indicator is used…

EquitiesChina marketsMomentumMean reversion
BigQuant

The post describes troubleshooting a strategy that behaved correctly in backtests but produced missing or intermittent signals in simulated trading. After checking other strategies and revisiting the selection conditions, the author found that including the…

BacktestingExecutionStatistics
BigQuant

The report discusses using the Black–Litterman (B-L) framework to incorporate an investment manager’s judgments into a quantitative factor portfolio. It frames factor information coefficients as the return inputs and their covariance matrix as a…

China marketsFactor investingPortfolio constructionMomentum
BigQuant

This Chinese-language summary outlines an equity market-timing approach based on a long short-term memory (LSTM) neural network. The model is described as learning dependencies across time-series observations and adapting to sharp changes in trends. It…

EquitiesMachine learningBacktestingChina markets
BigQuant

This brief summary outlines several approaches to rotating among equity industries. It discusses adapting the investment clock framework from asset allocation to sector selection, while noting that macroeconomic data may be delayed or unreliable and that…

EquitiesChina marketsFactor investingTechnical indicators
BigQuant

The document describes an equity strategy that combines broad-market timing with multiple pre-entry checks on each stock. It uses a dynamic portfolio of five holdings, with risk controls managed independently for each position. The stated rules include an 8%…

EquitiesRisk managementPosition sizingBacktesting
BigQuant

This Chinese-language post describes a method for turning raw futures tick updates into real-time minute bars for traders using lower- and medium-frequency strategies. Its design aims to align bar timestamps with common market-data conventions: ticks during…

FuturesMarket microstructureExecutionStatistics
BigQuant

The document summarizes research using account-level Shenzhen Stock Exchange data from 2012 to 2015 to examine whether daily price limits shape trading behavior. It separates individual investors by account size and compares them with institutions, focusing…

China marketsEquitiesMarket microstructureMean reversion
BigQuant

This research summary explains why monthly and year-over-year macroeconomic readings can mislead when economic series contain seasonal and calendar effects. It describes X-13-ARIMA-SEATS as a widely used adjustment approach and emphasizes that seasonal…

Multi-assetStatisticsChina marketsFactor investing
BigQuant

The document outlines a beginner workflow for creating an AI-assisted stock selection strategy on a quantitative platform. It starts with defining a trading style, translating that idea into candidate features, expressing those features for model training,…

EquitiesMachine learningBacktestingPosition sizing