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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

9,660 documents

Quant Q&A

The document outlines several ways to assess whether a candidate signal adds useful return information. Suggested checks include risk-adjusted performance and tail risk, regression against established factor models to examine the intercept, trading costs and…

Factor investingStatisticsBacktestingExecution
Quant Q&A

The document considers vendors for global equity trade bars at intraday frequencies, with the goal of researching short-horizon market patterns and using live data to trade resulting strategies. It identifies historical coverage, a real-time feed, and access…

EquitiesHigh-frequency tradingMarket microstructureExecution
Quant Q&A

The discussion compares buying weekly deep in-the-money calls and rolling them each expiration with buying a call about a month out. It asks which approach minimizes time value paid per day, assuming implied volatility stays constant. One response suggests…

OptionsDerivatives pricingExecution
Quant Q&A

The document explains “getting picked off” as an informal market microstructure term rather than a condition with a universal empirical definition. In high frequency trading and market making, it often refers to a faster, better informed participant trading…

Market microstructureHigh-frequency tradingMarket makingExecution
Quant Q&A

The document explains delta as the sensitivity of an option’s value to a change in the underlying asset’s value. A quoted 50 delta corresponds approximately to a 0.5 change in option value for a one-unit change in the underlying, making delta useful for…

OptionsDerivatives pricingVolatilityExecution
Quant Q&A

The document examines an hourly machine-learning signal that predicts the direction of returns measured from an interval’s average price. The key timing issue is that the signal becomes available only after that interval ends, so a backtest that assumes…

Machine learningBacktestingExecutionStatistics
Quant Q&A

The document compares options for asynchronous, non-blocking TCP and UDP networking in C++, framed around low-latency and real-time development. It notes that standard networking support was expected in C++17 and presents Boost as a way to abstract…

High-frequency tradingExecutionMarket microstructure
Quant Q&A

The document presents reserve price and reserve bid and ask expressions from the Avellaneda–Stoikov market-making strategy. The reserve price shifts from the market midpoint according to the trader’s inventory, risk aversion, price volatility, and remaining…

Market makingExecution
Quant Q&A

The document discusses how to obtain historical opening auction prices for S&P 500 stocks, distinguishing the auction print from a generic first trade. It describes primary exchange price data as a source of opening and closing auction prices, since the…

EquitiesMarket microstructureExecutionBacktesting
Quant Q&A

This note considers replacing a long stock and short stock position in a pairs trade with synthetic option positions. It asks whether the synthetic exposure has the same risk and reward as holding the underlying shares directly. The response suggests that…

Pairs tradingEquitiesOptionsExecution
Quant Q&A

The document compares storage approaches for large options and time-series tick datasets used primarily in backtesting. When the dominant access pattern is reading sequentially between a start and end time, flat files with binary encoding can avoid the…

OptionsBacktestingExecution
Quant Q&A

The document considers how a market maker with no initial inventory might submit orders to capture the bid-ask spread, and whether submitting both sides is preferable to sequential or one-sided strategies. One response explains that market makers generally…

Market makingExecutionMarket microstructurePosition sizing
Quant Q&A

The document describes where C++ is used in high-frequency trading and why firms rely on it. The main use is the latency-sensitive path that receives market data, updates forecasts or risk information, makes a trading decision, and sends, changes, or cancels…

High-frequency tradingExecutionMarket microstructureBacktesting
Quant Q&A

The document explains portfolio terminology through examples involving dividend reinvestment, depositing cash, and selling one asset to buy another. It presents a portfolio as a collection of asset positions whose composition can change over time. In…

Portfolio constructionExecutionRisk management
Quant Q&A

The document distinguishes exchange liquidity provision and retail broker execution from agency trading performed for a named client. It explains that identifiers in options FIX messages, including fields such as ClientID and CustomGroupID, support tracking…

OptionsExecutionMarket microstructure
Quant Q&A

The document addresses a practical obstacle in backtesting short-equity strategies: historical share availability and borrow costs can determine whether a proposed short trade was feasible and profitable. It suggests asking a prime broker for historical…

EquitiesBacktestingExecutionRisk management
Quant Q&A

The discussion compares C++, C#, Java, and Python for quantitative finance and argues that language choice depends on the work being done. C++ is described as common for application backbones and computationally demanding tasks such as derivative pricing,…

High-frequency tradingMachine learningExecutionStatistics
Quant Q&A

The document compares two ways to run a prediction market: a limit order book, where participants post orders that wait for counterparties, and an automated market maker that quotes prices and accepts trades. Order books are described as straightforward to…

Market makingMarket microstructureExecutionRisk management
Quant Q&A

The document explains how to convert a fixed-width exchange data feed that mixes text and binary fields into a readable tabular format. It recommends using the feed specification as a field-by-field map, then processing each record as bytes and converting…

Market microstructureEquitiesExecution
Quant Q&A

The document collects responses to a trader seeking a Python interface for automating non-high-frequency stock strategies. The trader already has an Interactive Brokers account and asks whether another broker offers a better supported API than unofficial…

EquitiesExecutionBacktesting
Quant Q&A

The note explains how a portfolio turnover formula separates weight changes caused by trading from changes caused by asset returns. It first estimates each stock’s end-of-month weight under a no-trading assumption: its starting weight is adjusted by its…

EquitiesPortfolio constructionExecution
Quant Q&A

The document explains why electronic order behavior cannot be learned from one universal description. Exchanges differ in the order types they support and in their matching rules, which determine matters such as queue priority and the effects of order…

Market microstructureExecutionHigh-frequency trading
Quant Q&A

The document compares ways to represent an order book while supporting price ordering, fast updates, and access to the best bid and ask. For order-based equity books, one detailed proposal combines an order-ID lookup structure with metadata linking each…

Market microstructureExecutionHigh-frequency tradingEquities
Quant Q&A

The document explains that a trade does not have to move the mid-price when the mid is defined as the average of the best bid and best ask. If the top of book has enough displayed size, a market order smaller than that size can execute without removing the…

Market microstructureExecution