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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

3,481 documents

BigQuant

This report introduces an early-stage study of market microstructure signals in the IF equity index futures contract. It proposes examining the difference between midpoint and trade-price log returns, quoted depth, large trades, and signed volume. The…

FuturesMarket microstructureStatisticsHigh-frequency trading
BigQuant

This document reviews an empirical comparison of machine learning approaches for forecasting monthly returns in China’s A-share market. The described process trains models on prior data, uses a validation period for tuning, and tests predictions out of…

China marketsEquitiesMachine learningFactor investing
BigQuant

This research summary reviews tests of eight proprietary price-volume factors in Chinese equities, comparing monthly, weekly, and biweekly rebalancing. The factors include active buying and selling, smart money, ideal amplitude, APM, ideal reversal, large-…

EquitiesChina marketsMomentumTechnical indicators
BigQuant

The document outlines the Chaikin oscillator, a technical indicator built from price and volume data. It first weights volume by the relationship between the close and open relative to the day's high-low range, cumulatively sums that measure, and then takes…

Technical indicatorsStatisticsEquities
BigQuant

This tutorial describes a Python workflow for comparing two subfunds with the China Securities 1000 Index using weekly net asset values. It calculates rolling correlations over a 20-day window and plots how those relationships change, then compares period…

China marketsStatisticsRisk managementPortfolio construction
BigQuant

This example describes a daily, long-only futures strategy using a 10-period average crossing a 30-period average to generate entry and exit signals. A 120-period moving average acts as a trend filter: the strategy opens a position after an upward crossover…

FuturesTrend followingTechnical indicatorsBacktesting
BigQuant

The document outlines a workflow for building equity strategies that adapt their stock-selection logic to market conditions. It proposes measuring market sentiment with signals such as the number of limit-up stocks and average returns, then adding industry…

EquitiesFactor investingStatisticsBacktesting
BigQuant

This post is a satirical warning about trusting impressive quantitative strategy claims and backtest curves. It points readers toward shared deep-learning strategy code, but supplies no description of the model, training data, signals, or validation process.…

BacktestingRisk managementPosition sizing
BigQuant

This Chinese market-timing report reviews sector strength, valuation, breadth, moving-average structure, and equity-fund positioning around July 2020. Its summary highlights stronger recent breadth: the share of stocks reaching 60-day highs and the balance…

EquitiesChina marketsMomentumTechnical indicators
BigQuant

The article presents a robust version of equal risk contribution (risk parity) portfolio construction. It treats the estimated covariance matrix as uncertain and builds a bounded uncertainty set around the estimate. The optimization then accounts for…

Portfolio constructionRisk managementStatisticsBacktesting
BigQuant

This research note describes an enhancement strategy for China’s CSI 1000 small-cap equity index. It combines factors representing valuation, growth, trading behavior, capital flows, and network relationships within index constituents. After neutralizing for…

EquitiesFactor investingPortfolio constructionBacktesting
BigQuant

This research summary describes how to build and compare active and passive portfolios of Chinese healthcare-themed funds. For active funds, it argues that screening should use actual underlying holdings, since a fund's label may not reflect a concentrated…

EquitiesChina marketsPortfolio constructionBacktesting
BigQuant

The document discusses whether stochastic-process mathematics, including Brownian motion, still matters as machine learning becomes more common in quantitative finance. It presents several practitioner views: stochastic calculus has a stronger role in…

Machine learningStatisticsDerivatives pricingExecution
BigQuant

This 2018 research summary forecasts changes to the FTSE China A50, an index of large Chinese mainland stocks selected with free-float and liquidity considerations. It describes quarterly reviews in March, June, September, and December, and identifies index…

EquitiesChina marketsEvent-drivenPortfolio construction
BigQuant

This commentary examines sharp style swings in China’s A-share market, using recent surges and reversals in the nonferrous metals sector as its main example. It argues that a rapid rise concentrated in a small number of stocks can pull liquidity from other…

China marketsEquitiesMarket microstructureSentiment
BigQuant

This overview distinguishes language generation from financial prediction, arguing that strong text generation does not by itself solve markets’ low signal-to-noise ratios, changing patterns, and limited data. It explains GPT’s development and instruction…

Machine learningStatisticsBacktestingDerivatives pricing
BigQuant

The document outlines a daily trend-following strategy for commodity futures based on Bollinger bands. It enters long when price breaks above the upper band and short when price breaks below the lower band, then exits when price crosses back through the…

FuturesCommoditiesTrend followingBreakout
BigQuant

This weekly review compares style-factor performance across Chinese equity universes and summarizes quantitative public-fund returns against broader stock and equity-oriented funds. It reports that price and trading activity related factors—including…

EquitiesChina marketsFactor investingMean reversion
BigQuant

The article describes a method for estimating consensus net profit from analyst forecasts and using that estimate to build equity selection signals. It weights forecasts by report recency and by each analyst’s prior adjusted forecast error, giving greater…

EquitiesFactor investingStatisticsBacktesting
BigQuant

This opinion article argues that retail traders face a structural disadvantage in short-term equity trading because quantitative firms can use fast systems and direct market connectivity. It attributes the gap to execution speed and claims that automated…

EquitiesExecutionMarket microstructureRisk management
BigQuant

The article describes REDD-COPS, a dynamic allocation method designed to constrain rolling portfolio drawdown while pursuing long-term growth. It replaces a lifetime peak-to-trough reference with a rolling economic maximum, then adjusts exposure to risky…

Multi-assetPortfolio constructionRisk managementVolatility
BigQuant

This short forum exchange addresses how to adapt a BigQuant strategy to rebalance on a five-day schedule. The reply recommends defining the holding-period setting in the backtest module’s initialization function, then using the K-line processing function to…

EquitiesBacktestingPortfolio construction
BigQuant

This article explains why high-frequency trading firms invest heavily in reducing the time it takes to receive market information and send orders. It describes an earnings-news episode in which an automated firm traded before financial news terminals…

High-frequency tradingExecutionMarket microstructure
BigQuant

The document introduces an MQL4 library that ports regular-expression functionality from the .NET Framework. It outlines the library’s components for patterns, matches, capture groups, options, and Unicode character categories. It also lists options such as…

Execution