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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
WonderTrader
14 documents
Alphalens
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

1,116 documents

Quant course library

This guide explains two ways to schedule asynchronous work in an event-driven trading application. A loop-run task registers an asynchronous callback at a specified interval, measured in seconds, and returns an identifier that can later be used to unregister…

ExecutionHigh-frequency trading
Quant course library

The document explains how a trading application can use a remote procedure call (RPC) service to share events and handle requests across separate processes. It frames RPC as a way to work around Python’s global interpreter lock limiting CPU-bound work in a…

ExecutionMarket microstructureHigh-frequency trading
Quant Q&A

The document outlines several mechanisms that can produce negative autocorrelation in short-horizon returns. The classic explanation is bid-ask bounce: trades initiated by buyers and sellers alternate between ask and bid transaction prices, creating…

EquitiesHigh-frequency tradingMarket microstructureStatistics
Quant Q&A

The document outlines ways high-frequency traders can structure equity trading to manage commissions and exchange fees. One route is direct exchange access through a broker identifier, allowing a firm to pay or receive the exchange’s active or passive fees…

High-frequency tradingExecutionMarket microstructureEquities
Quant Q&A

The discussion asks whether high-frequency foreign-exchange ask quotes are considered less reliable than bids. The answers reject that general rule: they describe FX quotes as actionable and argue that both sides are needed to construct a mid-price and then…

ForexMarket makingMarket microstructureHigh-frequency trading
Quant Q&A

The document concerns imbalance bars and run bars, sampling methods discussed in Advances in Financial Machine Learning. The author is implementing them and asks for further literature, explaining that the material consulted so far emphasizes volume…

Market microstructureExecutionHigh-frequency trading
Quant Q&A

The document distinguishes a speed advantage in reacting to market data from arbitrage in the stricter sense of exploiting price differences. It describes how direct exchange feeds can reveal quote changes before a slower consolidated feed, allowing a…

EquitiesHigh-frequency tradingArbitrageMarket microstructure
Quant Q&A

The document questions whether standard GARCH models adequately describe one-minute Bitcoin returns. The sample is characterized by frequent zero returns, low return magnitudes, and apparent microstructure noise; the author also reports that squared…

CryptoVolatilityHigh-frequency tradingMarket microstructure
Quant Q&A

The document describes the inventory challenge faced when quoting many option strikes and levels in a fast market. Frequent fills change the portfolio’s delta, but immediately hedging every fill with taker orders can incur substantial spread costs. Waiting…

OptionsHigh-frequency tradingMarket makingRisk management
Quant Q&A

The document explores why historical replay alone may not faithfully test market-making or other microstructure strategies. A replay uses recorded order-book events, but cannot fully capture how other participants would react to the strategy's orders. The…

Market makingMarket microstructureHigh-frequency tradingBacktesting
Quant Q&A

Realized variance is an estimate of intraday variation, not by itself a model for forecasting volatility across days. To forecast future volatility, a model must specify how information from realized measures relates over time. The document describes…

VolatilityStatisticsHigh-frequency tradingRisk management
Quant Q&A

The document discusses whether stochastic-control models built around the Hamilton–Jacobi–Bellman equation have practical value in market making and algorithmic trading. It describes their role as research and teaching tools for reasoning about quote widths,…

Market makingHigh-frequency tradingExecutionMarket microstructure
Quant Q&A

The discussion distinguishes computational limits by workload. For real-time trading, it argues that network throughput is often the constraint, while historical analysis is more commonly limited by memory capacity or bandwidth; ordinary CPU use may not be…

High-frequency tradingExecutionMarket microstructureDerivatives pricing
Quant Q&A

The discussion argues that the right graduate education for quantitative finance depends on the work a person wants to do. Financial engineering programs may be especially relevant for valuation and pricing model development, while quantitative trading has…

Machine learningStatisticsHigh-frequency trading
Quant Q&A

The document explains why high-frequency trading can remain relevant when a market allocates fills pro rata, reducing the importance of queue position. It distinguishes liquidity provision from liquidity taking: speed may matter less for winning a place in…

FuturesHigh-frequency tradingMarket microstructureExecution
Quant Q&A

The document compares Excel DDE and ActiveX connections for streaming market quotes and explains why an RTD server may be a better option when faster spreadsheet updates are needed. RTD supports a configurable refresh interval, including millisecond…

ExecutionMarket microstructureHigh-frequency trading
Quant Q&A

The exchange explains what trade, quote, and order book feeds reveal about order types. Public feeds generally do not label market orders or iceberg orders. Matching trades with identical timestamps may help approximate market order activity, but it can…

Market microstructureExecutionHigh-frequency trading
Quant Q&A

The document discusses whether QuickFIX is fast enough for an order management system and argues that the answer depends on measured requirements and the full processing architecture. A slow component elsewhere, including network communication, can dominate…

ExecutionMarket microstructureHigh-frequency trading
Quant Q&A

The document compares ways to calculate a bond’s yield to maturity from price when repeated updates make speed important. One approach computes yields for three selected prices, fits a quadratic price–yield relationship, and uses it to estimate yields for…

Fixed incomeDerivatives pricingExecutionHigh-frequency trading
Quant Q&A

The document asks whether hourly observations can be used to estimate dynamics that matter over a horizon of several days, particularly mean reversion or cointegrating relationships. It refers to a proposed analogy between estimating monthly relationships…

StatisticsMean reversionHigh-frequency trading
Quant Q&A

The discussion models exchange round-trip latency as the sum of network transit time and matching-engine message-processing time. For venues in separate data centers, network distance and routing usually dominate: fiber routes are indirect, switches and…

EquitiesExecutionMarket microstructureHigh-frequency trading
Quant Q&A

The document considers why a stock price can move repeatedly even when company news does not arrive at the same pace. It presents price as a noisy proxy for underlying value: after news, market participants may take time to interpret its implications and…

EquitiesMarket microstructureHigh-frequency tradingStatistics
Quant Q&A

The note explains why drift is often neglected when modeling asset prices at very short time intervals. For geometric Brownian motion with constant drift and volatility, it compares how each component scales as the interval shrinks: drift scales linearly…

High-frequency tradingStatistics
Quant Q&A

The document asks how to select the optimal bandwidth for a realized-kernel volatility estimate when the desired output uses one-minute bins. It gives the bandwidth formula from the cited reference and notes that its noise-to-signal input uses integrated…

High-frequency tradingVolatilityStatisticsMarket microstructure