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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

170 documents

Quant Q&A

The document examines an option whose payoff and premium are expressed in the underlying asset, using an ETH example to compare conversion from a conventional Black–Scholes value with a direct simulation. The key issue is the payoff definition: converting…

OptionsCryptoDerivatives pricingStatistics
Quant Q&A

The document examines a reported average duration of roughly 25 minutes for continuous ETH price rises or falls, measured in five-minute intervals over three months. The response recommends defining what would count as unusual and comparing the observation…

CryptoStatisticsBacktestingMarket microstructure
Quant Q&A

The document explores exit choices for a crypto pairs trade entered after finding a cointegrated relationship. The trader reports that cointegration tests can stop indicating a relationship and later signal it again, while a simple exit at zero z-score has…

CryptoPairs tradingMean reversionRisk management
Quant Q&A

The document examines whether a Hurst exponent above 0.5 can justify shorting crypto futures after prices have fallen. The author estimates the exponent from 1,025 hourly mark-price observations for four Binance trading pairs and reports values around 0.62.…

CryptoFuturesTechnical indicatorsStatistics
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 addresses backtesting crypto strategies that calculate indicators on daily candles while processing finer-grained data for entries, exits, and risk controls. Its accepted answer recommends using Backtrader’s data replay feature with minute data…

CryptoBacktestingTechnical indicatorsExecution
Quant Q&A

The document responds to an observation that volatile Bitcoin prices seem to form a Weierstrass-like pattern across candlestick time scales. It explains that rough, fractal-like paths are not unique to cryptocurrency: Brownian motion and related stochastic…

CryptoStatisticsVolatilityMean reversion
Quant Q&A

The document raises a practical issue in a cryptocurrency perpetual-swap pair-trading strategy: a spread estimated by regression on log returns can produce opposite signals in consecutive observations. The author calculates the spread from rolling historical…

CryptoPairs tradingPerpetual futuresMean reversion
Quant Q&A

The discussion considers what market data to collect when building a Bitcoin price feeder for backtesting. Its practical recommendation is to begin with level-one data, such as trades and best bid and offer, and add order-book data only when a specific…

CryptoBacktestingMarket microstructure
Quant Q&A

The document presents an interview scenario in which an exchange OTC desk must handle a corporate request to trade a very large quantity of bitcoin within a narrow band around the mid-market price. The requested size is several times the exchange’s stated…

CryptoExecutionMarket microstructureRisk management
Quant Q&A

The document lists a small set of quantitative trading competitions in response to a Singapore-based participant seeking opportunities beyond the International Quant Championship and SMU Alphathon. It mentions a stock-prediction model competition run by XTX…

EquitiesForexCryptoMachine learning
Quant Q&A

The discussion considers how to allocate capital to an automated crypto strategy whose backtest suggests substantial drawdowns, and when to scale its exposure or stop trading as its edge may fade. The response emphasizes that changing position size is a…

CryptoPosition sizingRisk managementBacktesting
Quant Q&A

This document raises a question about why a five-day exponential moving average can differ when calculated from hourly closes versus minute closes. The finer series contains more observations over the same calendar span, and the author reports seeing…

Technical indicatorsStatisticsCrypto
Quant Q&A

This document explains how to allocate a market-cap-weighted crypto index when no constituent may exceed a chosen maximum weight. It presents a sequential calculation: start with each asset’s share of total market value, scale that share by the weight and…

CryptoPortfolio constructionPosition sizingStatistics
Quant Q&A

The document asks whether a long position in a quarterly bitcoin future and a short position in a perpetual swap can remain market neutral. It explains that perpetual swaps have no expiry and use funding payments to help keep their prices near spot. The…

CryptoFuturesPerpetual futuresCarry
Quant Q&A

The document raises practical questions about using XGBoost to predict whether the next candle’s closing price will move above or below a chosen threshold from the current open. The proposed feature set combines market sentiment, macroeconomic variables,…

CryptoMachine learningBacktestingTechnical indicators
Quant Q&A

The document asks why a one-year Bitcoin futures contract can trade at a premium whose annualized rate exceeds a comparable government bond yield, despite standard cost-of-carry relationships. It considers whether demand for leveraged exposure may help…

CryptoFuturesCarryMomentum
Quant Q&A

The discussion asks whether Bitcoin has a quantifiable fair value and how mispricing might be recognized. The answers describe its traded price as an outcome of supply and demand once the market is sufficiently liquid, while emphasizing that speculation is…

CryptoSpot marketsOptionsVolatility
Quant Q&A

The document asks whether reliable foreign-exchange prices are available during the weekend, when conventional spot markets are largely closed. The response cautions that quotes visible during quiet hours can be stale, making them poor evidence of current…

ForexCryptoSpot marketsFutures
Quant Q&A

The discussion surveys proposed links between chaos theory, nonlinear dynamics, fractals, and financial markets. It points to research on nonlinear dynamics, self-similarity, the Hurst exponent, and the use of fractal ideas to model asset returns. One…

StatisticsRisk managementMachine learningCrypto
Quant Q&A

The document raises a modeling question about applying the heterogeneous autoregressive realized volatility model to forecast one-minute realized volatility in an active Bitcoin market. The proposed regressors are realized volatility measured over recent…

CryptoVolatilityMarket microstructureBacktesting
Quant Q&A

The document examines a hypothetical collapse in USDT’s value and considers its consequences for Bitcoin holders and market pricing. It distinguishes direct self-custody, exchange balances, and exposure through financial intermediaries. Self-custody does not…

CryptoSpot marketsMarket microstructureRisk management
Quant Q&A

The note explains how collateral and funding payments relate to liquidation in perpetual futures. In the answer’s account, a position is liquidated when collateral falls below the maintenance margin. Collateral includes the amount initially posted, realized…

CryptoFuturesPerpetual futuresRisk management