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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

19,452 documents

Amberdata research

This market recap reviews Bitcoin and Ethereum options after approval of an Ethereum exchange-traded fund. It discusses realized and implied volatility, term structure, relative volatility, and option skew. The account says implied volatility fell as event…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

The article explains why implied volatility is an option price input solved from a pricing model, rather than a direct forecast of future realized volatility. It recommends assessing current IV against its own history and reading it alongside the shape of…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

This weekly market note connects Federal Reserve expectations and US economic releases with crypto and equity volatility. It argues that rates and inflation data could influence risk assets, then compares Bitcoin and gold implied volatility as a way to frame…

CryptoOptionsVolatilityDerivatives pricing
SuperMind

This overview compares four crypto trading styles by holding period, activity, and typical analytical focus. Scalping seeks frequent small moves within a day and demands close monitoring; day trading holds positions for hours but generally closes them before…

CryptoTechnical indicatorsRisk managementPosition sizing
FMZ forum

This tutorial explains how a JavaScript template on the FMZ platform can add custom modules to a visual Blockly strategy editor. It describes the module definition fields, including the block type, displayed label, input arguments, generated code, output…

Technical indicatorsExecutionCrypto
NautilusTrader

This documentation explains platform support for listed, crypto, spread, and binary options, including differences in their metadata and identifiers. It describes subscribing to venue-provided Greeks either for an individual contract or for a series-level…

OptionsCryptoDerivatives pricingBacktesting
Amberdata research

The guide explains how impermanent loss arises when the relative prices of two tokens change while they are held in an automated market maker (AMM) liquidity pool. It contrasts a liquidity position with simply holding the deposited assets, describes how…

CryptoDeFiRisk managementBacktesting
FMZ forum

This document outlines a basic program structure for a long-only spot cryptocurrency strategy. It separates the trading signal from the operational framework: the example has manual commands to open or close a position, while the user is expected to add the…

CryptoSpot marketsExecutionPosition sizing
BigQuant

This review summarizes research spanning equity market microstructure, institutional holdings, crypto momentum, dynamic-graph forecasting, and reinforcement learning for limit-order-book trading. In equities, one study groups trades by their timing relative…

EquitiesCryptoMomentumMachine learning
Amberdata research

This research roundup describes several quantitative finance studies. One classifies equity trades by their short-term co-occurrence with other trades and standardizes associated order imbalances into conditional order imbalance measures. These measures…

EquitiesCryptoMomentumMachine learning
Amberdata research

The document introduces blockchain oracles as software that retrieves off-chain information and makes it available to smart contracts. Because a blockchain cannot directly query external APIs or data feeds, an oracle provides a link between on-chain…

CryptoDeFiDerivatives pricing
Kraken Learn

The article compares custodial wallets, where a provider controls private keys, with non-custodial wallets, where users control and protect them. Custody by a platform can simplify setup, account recovery, and internal transfers. It also exposes users to…

CryptoSpot marketsDeFiRisk management
Hummingbot docs

This guide explains how to connect Hummingbot to Injective Helix for spot or perpetual trading, with a focus on delegated accounts. It describes separating a portfolio wallet that holds funds from a trading wallet that receives authority to trade those…

CryptoExecutionMarket makingPerpetual futures
Freqtrade docs

This guide explains how FreqAI trains and deploys adaptive machine-learning models in live or dry trading and in historical backtests. Live operation can retrain models as capacity permits, use the latest trained model for predictions, and apply limits on…

Machine learningBacktestingCryptoRisk management
Cryptohopper blog

This article explains how to combine centralized exchange market data with blockchain data through multiple Model Context Protocol servers. Exchange tools provide prices, candles, and order books; on-chain tools can reveal transfers, holders, decentralized…

CryptoOn-chain dataMarket microstructureExecution
Amberdata research

This weekly crypto options analysis links macroeconomic events, including US debt ceiling negotiations, inflation data, Fed minutes, and upcoming employment figures, to Bitcoin and Ethereum volatility. It argues that the market may remain in a wait-and-see…

CryptoOptionsVolatilityCarry
Amberdata research

This market snapshot examines the February 2024 crypto rally through spot-market activity, ETF flows, decentralized exchange volumes, lending deposits, miner supply behavior, and Bitcoin net unrealized profit and loss (NUPL). It presents the view that ETF…

CryptoOn-chain dataSentimentMarket microstructure
Amberdata research

This overview explains a set of Ethereum indicators grouped around market tops and bottoms, sentiment, and user activity. Price measures include the 50-day and 200-day moving averages, a 200-week average, and the Pi Cycle comparison between the 111-day…

CryptoOn-chain dataTechnical indicatorsSentiment
Amberdata research

This report examines BTC options and futures behavior in Q1 2023, then introduces Lyra as an automated market maker for on-chain options. It contrasts AMM and order-book models and frames DeFi options as an evolving market without an established analytical…

CryptoOptionsVolatilityDerivatives pricing
Hummingbot docs

This controller monitors liquidation activity for a selected Binance perpetual futures pair and initiates a dollar-cost averaging trade when liquidations on the chosen side exceed a configured quote-currency threshold within a time window. It treats forced…

CryptoPerpetual futuresMean reversionPosition sizing
Amberdata research

This study builds separate XGBoost models to predict next-day Ethereum volatility from activity in the USDC, USDT, and DAI lending ecosystems. Features include lagged borrowing, repayment, and flash-loan measures, plus seven-day averages. It uses time-series…

CryptoDeFiVolatilityMachine learning
OctoBot

This document explains OctoBot’s evaluator layer, which computes trading signals and routes them to strategies and trading modes. It distinguishes evaluators triggered by closed candles, forming candles, external social feeds, user scripts, and strategy…

CryptoTechnical indicatorsExecutionBacktesting
Amberdata research

The document explains how to use spot market volume data to assess activity, liquidity, exchange concentration, and trading interest across tokens and quote currencies. It describes comparing dollar volume over time, reviewing each exchange’s share, tracking…

CryptoSpot marketsMarket microstructureSentiment
FMZ forum

This support post describes a reported failure in an automated XRP withdrawal after a Binance maintenance update. The user received an error indicating an unauthorized IP address, although they said the IP remained on the exchange’s allowlist. The original…

CryptoExecutionSpot markets