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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 asks how asset managers track fixed income indexes whose holdings may number in the hundreds. It identifies characteristics that can guide a representative sample, including duration, credit quality, coupon, maturity, country, and bond type. It…

Fixed incomePortfolio constructionExecution
Quant Q&A

The answer considers whether banks and asset managers are likely to develop trading algorithms in-house or rely on specialist firms. It argues that either path may be difficult within traditional institutions, citing gaps in management’s quantitative…

High-frequency tradingMachine learningExecutionMarket microstructure
Quant Q&A

The document clarifies the distinction between Bloomberg’s open source data distribution technology and access to Bloomberg’s data. The API and its documentation are publicly available, but connecting to Bloomberg’s database and downloading its data requires…

Multi-assetExecution
Quant Q&A

The document discusses where to obtain official real-time and intraday market data for venues such as US stock exchanges and CME. It distinguishes free, limited ways to explore data from the paid access typically required for raw exchange feeds. Suggestions…

Market microstructureExecutionEquitiesFutures
Quant Q&A

The discussion explains why executions recorded for an order in LOBSTER data can share the order’s submitted limit price. A buy limit order resting at 100 may be filled by incoming sell orders at that price, or it may execute against several resting sell…

EquitiesExecutionMarket microstructure
Quant Q&A

The document weighs R and Python for quantitative finance tasks, including time-series data handling, statistical modeling, mathematical computation, visualization, derivatives pricing, system integration, and deep learning. Its answers describe different…

StatisticsHigh-frequency tradingMachine learningExecution
Quant Q&A

The document asks how a two-sided market maker can use a signed forecast of the next price movement, expressed in ticks, to reduce exposure when prices begin to trend. Its concern is that adverse directional moves can make inventory losses grow faster than…

Market makingMarket microstructureExecutionMomentum
Quant Q&A

The document asks how to backtest and plot a strategy whose signals indicate long, flat, or short exposure, using historical prices. The proposed calculation forms log price returns, multiplies them by the prior period’s signal to represent the position held…

BacktestingExecutionStatistics
Quant Q&A

The document examines why paired leveraged and inverse ETFs can have different daily returns even when their stated goals are equal and opposite multiples of an index’s daily performance. It distinguishes the index level, the fund’s net asset value based on…

EquitiesRisk managementVolatilityExecution
Quant Q&A

The document asks whether a trading application must establish a FIX quote session and subscribe to market data before sending a new order over an order session. The answers distinguish protocol rules from a particular broker’s workflow: FIX itself does not…

ForexExecutionMarket microstructure
Quant Q&A

The document answers whether shares of a company listed on Nasdaq can also trade on the NYSE and whether a specific law enabled that activity. It attributes broader cross-market trading access to the NYSE’s merger with Arca, while stating that no particular…

EquitiesExecutionMarket microstructureUS markets
Quant Q&A

The document addresses how to retrieve a changing set of valid security codes from Bloomberg in Excel, especially when option strikes and expirations change. Instead of manually listing possible strikes and building each option code, the accepted answer…

OptionsExecution
Quant Q&A

The document describes the basic market-making model in an ETF context. A market maker quotes prices at which it will buy and sell, generally buying at the bid and selling at the ask; the difference between those prices is a primary source of revenue.…

Market makingExecutionMarket microstructureEquities
Quant Q&A

The document asks how to estimate an execution or settlement price from a spot price when order size affects the realized price. It proposes using the bid–ask spread as a starting point because both spread and execution impact depend on liquidity, and asks…

CommoditiesExecutionMarket microstructureStatistics
Quant Q&A

The document explains how an exchange can handle a crossed book at the market open, when buy orders are priced above sell orders. Rather than giving priority simply to the highest bid or earliest order, the described opening process evaluates candidate…

EquitiesMarket microstructureExecution
Quant Q&A

The document explains why multiplying a futures contract size by its displayed price can produce a mistaken contract value. The key is the price unit: a soybean future may be quoted in cents per bushel rather than dollars per bushel. Contract size must be…

FuturesCommoditiesExecution
Quant Q&A

The answer distinguishes dealer-to-dealer (D2D) markets from dealer-to-client (D2C) markets to explain why some swap order books show indicative rather than executable prices. In D2D markets, the answer says executable broker systems or voice prices are…

Fixed incomeMarket makingMarket microstructureExecution
Quant Q&A

This discussion considers whether a candle-based strategy backtest can rely on mid-prices, especially when modeling stop losses, or should use bid and ask prices to reflect the side at which a trade executes. The author reports that replacing mid-price highs…

BacktestingExecutionMarket microstructureRisk management
Quant Q&A

The document asks how activity by multiple traders can affect temporary price impact on both sides of an order book. In the Almgren–Chriss framing, it distinguishes a joint permanent impact term, which depends on aggregate trading rates, from a common…

Market microstructureExecutionStatistics
Quant Q&A

Futures contracts are not generally paid for in full when opened; the settlement price supports daily mark-to-market valuation and final settlement at expiry. The document says exchanges often derive it from an average of late-day trades, reducing the impact…

FuturesExecutionMarket microstructureRisk management
Quant Q&A

The document describes how to distinguish order events in full-depth equity market data, particularly when preparing intraday records for analysis or machine learning. It maps message labels to their likely roles: adds on the bid or ask represent limit…

EquitiesMarket microstructureExecutionMachine learning
Quant Q&A

The document asks whether transaction records and candlestick summaries can reveal the highest bid, lowest ask, or quote sizes in the foreign exchange market. The available data includes trade times, amounts, prices, and bar-level measures such as highs,…

ForexMarket microstructureExecutionBacktesting
Quant Q&A

The document compares answers about downloading Bloomberg data through Excel and APIs, focusing on how request limits may apply and what can happen when they are reached. One explanation says limits count requests by ticker rather than by the number of…

ExecutionMarket microstructure
Quant Q&A

The note explains why quoted bid-ask spreads matter for investor transaction costs and why they do not capture every trade price. Trades can occur inside the displayed spread when another venue posts different prices, or when orders meet between the bid and…

Market microstructureExecutionEquitiesUS markets