Skip to content

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 considers how to quantify liquidity in listed equity, index, and ETF options for scanning or analysis. It recommends looking beyond contract volume to bid–offer spreads, option notional traded, changes in quotes relative to movements in the…

OptionsMarket microstructureExecution
Quant Q&A

The document explains that standard open, high, low, and close bars are constructed from transaction prices. Best bid and ask quotes, quote sizes, and aggregate volume or book-size fields do not provide the actual sequence of trades needed for true OHLC…

Market microstructureExecutionStatistics
Quant Q&A

P&L leakage is a small, recurring loss that may be hard to notice in daily results but accumulates into a material drag over time. The note places the concept in hedging and trading, where a reported or expected profit can omit costs and risks that are…

Risk managementExecutionVolatility
Quant Q&A

The document explains why a backtest based only on the best bid and best ask can overstate achievable execution quality. Those quotes represent the most favorable available prices for limited quantities; larger orders may consume that liquidity and fill at…

BacktestingExecutionMarket microstructure
Quant Q&A

The document discusses a lower-cost approach to storing and querying market tick and bar data when specialized financial databases are too expensive. The response suggests using an OLAP server with cube-based aggregation. It describes persisting ticks at…

Market microstructureExecutionBacktesting
Quant Q&A

The document considers why trading firms might locate near Bloomberg’s data center and relay information to New Jersey over microwave links. It distinguishes market news from information available through Bloomberg’s electronic trading platform, EMSX. The…

ExecutionMarket microstructureHigh-frequency trading
Quant Q&A

The answer recommends separating operational monitoring from strategy monitoring when running automated trading systems. Operational checks include server heartbeats and alerts, centralized log persistence, and visibility into CPU and memory use. It…

ExecutionRisk managementMarket microstructureBacktesting
Quant Q&A

The discussion considers whether short-term directional strategies may work better in markets with more uninformed traders. The response says uninformed flow can be valuable because it is less informed about near-term prices, but does not develop a strategy…

Market microstructureExecutionHigh-frequency trading
Quant Q&A

The document describes a practical signal-processing problem in a trading system that receives live bid and ask data. The author reports that offline backtests can use non-causal smoothing methods with access to future observations, but those methods lag…

Technical indicatorsTrend followingBacktestingExecution
Quant Q&A

The discussion examines why observed broker spreads across currency pairs may look counterintuitive and whether spreads are useful as a rough measure of trading cost. One explanation is a quotation-unit effect: comparing the dollar cost of one unit of each…

ForexExecutionMarket microstructure
Quant Q&A

The document outlines a way to reconcile instrument identifiers when OTC derivative trades arrive from systems using Bloomberg and Refinitiv symbols. Its proposed first check is to compare ISINs when both are supplied, then consult an internal lookup table…

Derivatives pricingExecutionMarket microstructure
Quant Q&A

The document discusses narrowing a large universe of candidate equity pairs after an Augmented Dickey–Fuller test and half-life calculation leave many candidates. Suggested filters include liquidity, trading costs, backtesting with cross-validation, and…

EquitiesPairs tradingMean reversionBacktesting
Quant Q&A

The document argues that risk management can sit beside an ML or AI alpha model rather than trying to interpret its internal features. A separate portfolio controller can translate forecasts or directional signals into feasible positions, accounting for…

Machine learningRisk managementPosition sizingBacktesting
Quant Q&A

This note discusses execution choices for an investor trading illiquid small-cap, micro-cap, or over-the-counter securities with holding periods of months or longer. The response recommends judging execution against the trading model’s intended fills and…

EquitiesExecutionMarket microstructureRisk management
Quant Q&A

The discussion considers how to make markets in assets with sparse trading, few resting orders, limited participants, high volatility, and wide spreads. Suggested price-discovery inputs include researching who holds the asset, identifying historical buyers…

Market makingMarket microstructureRisk managementExecution
Quant Q&A

The discussion compares accounts of quote data and order handling in the QuantConnect backtesting environment, while also mentioning Quantopian and other tools in the question. One answer says the platform lacked bid and ask data at the time described and…

BacktestingExecutionMarket microstructureEquities
Quant Q&A

The document considers whether Interactive Brokers data can be fetched directly by a hosted web application when its API requires Trader Workstation or IB Gateway to run as a separate executable. The responses argue that continuously collecting live or tick…

ExecutionMarket microstructureHigh-frequency trading
Quant Q&A

The document outlines practical challenges for a firm moving from vendor-provided end-of-day data to live feeds from exchanges. Direct access means handling exchange-specific formats and APIs, building and maintaining parsers, and arranging network…

Fixed incomeFuturesOptionsMarket microstructure
Quant Q&A

The document discusses how a student on a limited budget might obtain historical trade-by-trade prices and sizes for NYSE and Nasdaq stocks. The question distinguishes exchange-wide coverage from data limited to trades executed on a single venue, and notes…

EquitiesMarket microstructureExecution
Quant Q&A

The document explains how incoming buy orders can alter the best bid and ask in a limit order book. A buy priced below the best bid leaves the displayed best bid unchanged; one at that price adds quantity; and a more aggressive buy can establish a new best…

Market microstructureExecution
Quant Q&A

The discussion addresses how to assess an intraday equity strategy whose backtest shows about one cent of gross profit per trade before transaction costs. It emphasizes that Sharpe ratio and profit per share are useful, but neither establishes viability…

EquitiesBacktestingExecutionMarket microstructure
Quant Q&A

The document explains why a latency figure is meaningful only when its start and end points are specified. It distinguishes order-to-accept latency, the round trip from sending an order until receiving an acceptance, cancellation, or execution response, from…

High-frequency tradingExecutionMarket microstructure
Quant Q&A

The discussion surveys software and methods for testing options strategies, including multi-leg spreads. It explains why this work is harder than testing linear instruments: historical option chains are large, implied volatility histories may be unavailable,…

OptionsBacktestingVolatilityExecution
Quant Q&A

The document surveys liquidity mechanisms beyond a central limit order book, including requests for quote, streaming quotes, single-dealer platforms, multi-dealer platforms, systematic internalisers, and dark pools. The response focuses on the trade and…

Market microstructureExecutionFixed incomeMarket making