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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

766 documents

Amberdata research

This reference surveys machine-learning methods and relates them to investment research tasks. It covers linear and logistic regression, naive Bayes, nearest neighbors, support vector machines, decision trees and ensembles, neural networks, sequence models,…

Machine learningStatisticsEquitiesRisk management
Amberdata research

This market commentary reviews BTC and ETH options conditions ahead of expected ETH ETF trading. It reports falling realized volatility and softer one-month implied volatility, alongside positive carry that may appeal to traders expecting range-bound prices.…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

This market commentary links a stronger-than-expected U.S. jobs report and falling unemployment to reduced expectations for a large Federal Reserve rate cut. It discusses upcoming inflation releases as possible catalysts for further rate-market repricing,…

CryptoOptionsVolatilityEvent-driven
Amberdata research

This market commentary examines Bitcoin and Ether options alongside spot prices, macro events, and regulatory news as of April 23, 2023. It discusses dealer gamma, implied volatility, volatility risk premium, term structure, and trading flows. The author…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

This article explains how derivatives can hedge existing crypto holdings or express views on future prices, and how market data can inform those decisions. It describes monitoring open interest and trading volume for clues about liquidity and changes in…

CryptoDerivatives pricingRisk managementArbitrage
Amberdata research

This market snapshot discusses legal and regulatory developments alongside trading conditions across centralized exchanges, decentralized exchanges, lending protocols, and blockchain networks. It describes a court ruling in Grayscale’s challenge to the SEC…

CryptoDeFiExecutionMarket microstructure
Amberdata research

This research summary compares high-frequency factors built from minute data in stocks and futures. It groups signals into return-distribution measures, intraday volume patterns, price-volume relationships, order-flow measures, and trend strength. Reported…

EquitiesFuturesHigh-frequency tradingFactor investing
Amberdata research

This article explains how on-chain monitoring can help liquidity providers and DeFi teams detect contract risks and unusual pool activity. It outlines reentrancy, code flaws, and front-running as common vulnerabilities, then recommends tracking liquidity,…

CryptoDeFiOn-chain dataRisk management
Amberdata research

This weekly digital asset review combines regulatory and product news with market indicators for Bitcoin, Ethereum, and Solana. It discusses ETF holdings and weekly flows, futures open interest across exchanges, long-short ratios, and funding rates. The…

CryptoFuturesTechnical indicatorsSentiment
Amberdata research

This market commentary links Federal Reserve expectations and geopolitical events with moves in bonds, gold, equities, and crypto. It reports that markets were pricing a greater chance of the Federal Reserve holding rates, while gold continued rising amid…

CryptoOptionsVolatilityFutures
Amberdata research

The document introduces a charting feature for viewing how implied-volatility smiles have changed over recent time intervals and where current option pricing sits on those curves. The visualization is intended to help traders assess both the level of implied…

OptionsVolatilityDerivatives pricing
Amberdata research

The document explains how DeFi borrowers pledge collateral worth more than their loans, creating a buffer against falling crypto prices. It discusses how lending platforms set overcollateralization ratios according to collateral risk, market conditions, and…

CryptoDeFiRisk managementPosition sizing
Amberdata research

The post presents a Chinese equity screen combining daily price range, a ranking based on net large-order activity, and a minimum market-capitalization condition. Its stated rationale is to find stocks with notable volatility and trading activity while…

EquitiesChina marketsVolatilityTechnical indicators
Amberdata research

This market recap reviews BTC and ETH options conditions around a period of rising crypto prices and an approaching halving. It compares realized and implied volatility, term structures, call and put skew, options activity, and dealer gamma. The commentary…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

This midweek market recap summarizes changes in Bitcoin options conditions, with brief coverage of volatility, term structure, skew, option flows, and gamma positioning. It reports that realized volatility continued to rise, implied volatility farther out on…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

This report studies whether DeFi lending activity in USDC, USDT, and DAI is associated with Ethereum volatility. It estimates daily volatility with the Garman-Klass method, which uses open, high, low, and close prices, then applies Augmented Dickey-Fuller…

CryptoDeFiVolatilityStatistics
Amberdata research

This market snapshot reviews crypto developments around Bitcoin’s reported all-time high and the passage of the GENIUS Act, alongside ETF flows, corporate treasury activity, futures open interest, long-short ratios, and funding rates. It describes a market…

CryptoFuturesPerpetual futuresSentiment
Amberdata research

The document describes how machine learning is being used by institutional trading teams for execution, risk management, and signal research, with digital assets as a key setting. It argues that model quality depends on access to reliable, detailed data from…

Machine learningCryptoOn-chain dataMarket microstructure
Amberdata research

This podcast introduction describes a discussion with T3 Index founder Simon Ho about volatility indexes, options trading, and building an index business. It identifies Spike as an index derived from SPY options that competes with the Cboe VIX, and mentions…

OptionsVolatilityEquitiesCrypto
Amberdata research

This overview explains several crypto market data types and their uses. OHLCV bars summarize opening, high, low, and closing prices alongside traded volume; the post connects OHLC data to candlestick chart analysis. It describes VWAP and TWAP as aggregated…

CryptoMarket microstructureExecutionStatistics
Amberdata research

This mid-week derivatives recap summarizes conditions in Bitcoin and Ethereum options. It reports that realized volatility recovered during the week, while both assets’ volatility term structures moved lower; Bitcoin’s curve shifted further into contango. As…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

This webinar overview introduces several Bitcoin and Ethereum measures that researchers might turn into trading signals. It groups them into market-cycle or valuation indicators, including Pi Cycle, Realized Cap, and the Bitcoin Yardstick; sentiment measures…

CryptoOn-chain dataSentimentStatistics
Amberdata research

This market commentary argues that quiet spot trading, low realized volatility, and a steep options term structure favor a near-term short-volatility bias in Bitcoin and Ether. It notes that scheduled inflation releases and a potential Bitcoin spot ETF…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

This newsletter discusses crypto market behavior during a sharp tariff-driven selloff in equities. It compares volatility across equities, gold, and bitcoin, observing that bitcoin implied volatility had not risen as much as other measures at the time. The…

CryptoOptionsVolatilityMarket microstructure