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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

8,116 documents

BigQuant

This paper examines whether deep neural networks can learn delta hedging strategies for options when the idealized assumptions behind Black–Scholes—such as continuous trading and no transaction costs—do not hold. It compares recurrent neural networks,…

OptionsDerivatives pricingMachine learningRisk management
vn.py community

This forum exchange addresses an identifier error when setting up silver options in VeighNa. A user reports that using “ag” as the option portfolio name causes a KeyError during backtesting or simulated trading, despite having downloaded option data and…

OptionsCommoditiesFutures
Kraken Learn

The document explains the bear flag as a possible continuation pattern: a steep price decline forms the pole, followed by a brief consolidation inside a roughly parallel channel. Traders may watch for a break below the channel’s lower boundary as a…

CryptoTechnical indicatorsBreakoutFutures
BigQuant

This July 2020 market note assesses Chinese equities after a sharp period of volatility. It combines a medium-term view based on expected returns and improving corporate earnings with a short-term signal from the decline in 50ETF options implied volatility.…

EquitiesOptionsVolatilityPortfolio construction
Amberdata research

This episode recap discusses crypto market structure, macroeconomic themes, and options positioning across Bitcoin, Ethereum, and Solana. It describes subdued sentiment alongside recovering prices, contrasts crypto with equities and gold, and presents the…

CryptoOptionsVolatilitySentiment
Amberdata research

The document introduces crypto calls and puts, explaining the buyer’s rights and the seller’s obligations. It describes moneyness by comparing the strike with the underlying price, and outlines intrinsic value and time value. It also introduces delta, gamma,…

CryptoOptionsDerivatives pricingVolatility
Amberdata research

This weekly recap describes conditions in BTC and ETH options around a major BTC expiry. It reports that BTC realized volatility had declined, with seven-day realized volatility returning to the 30s. The term structures changed little: front-end volatility…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

This market review examines crypto options and volatility during the sharp January 2023 rally. It connects the post-inflation-data rise in Bitcoin and Ether with higher realized and implied volatility, a backwardated Bitcoin volatility term structure, and…

CryptoOptionsVolatilityMarket microstructure
vn.py community

This event outline introduces options volatility trading through pricing theory, portfolio profit and loss, and the role of delta hedging. It distinguishes historical volatility, implied volatility as reflected in option time value, and realized volatility…

OptionsVolatilityDerivatives pricingRisk management
SuperMind

This stock screen selects shares with an amplitude above 1, ranks them by the day’s auction amount, and keeps the top five if the company has an outstanding convertible bond. The document interprets the bond condition as a way to include financing…

EquitiesOptionsChina marketsTechnical indicators
Lumibot

The document describes an options vertical credit-spread workflow split between a research agent and a trading-and-risk agent. The research role identifies and documents a listed put or call spread from current evidence. The second role independently checks…

OptionsExecutionRisk managementBacktesting
Amberdata research

This article explains how to read implied-volatility skew in cryptocurrency options using the 25-delta risk reversal and butterfly. Risk reversal compares call and put implied volatility at matching deltas to show which side of the market is more bid; the…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

The document explains options trading volume as activity over a period and open interest as the number of outstanding contracts. It suggests reading the two together to gauge participation, liquidity, and possible trend strength or fading activity. Examples…

CryptoOptionsDerivatives pricingMarket microstructure
MQL5 code base

This document presents xoshiro256**, a fast pseudorandom number generator with a 256-bit internal state, as an option for trading software that needs repeated random draws. It outlines an interface for integer and floating-point values, booleans, normally…

StatisticsBacktestingOptionsMachine learning
Amberdata research

This market commentary reviews Bitcoin and Ethereum options after a rally and a major political event. It reports that realized volatility rose ahead of the event, while front-end implied volatility later fell as the event passed without major crypto policy…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

This weekly review links the July 2023 macro backdrop to crypto options pricing and reported trading flows. It describes low realized volatility, steepening futures contango, and a sizable variance risk premium, arguing that low implied volatility alone did…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

The article describes how crypto derivatives data can help traders assess uncertainty around elections. It focuses on implied volatility as a measure of expected price swings and open interest as an indication of outstanding positions and participation. It…

CryptoOptionsVolatilityDerivatives pricing
vn.py community

A VeighNa user investigates why a near-the-money copper futures call displays a delta above two, comparing it with a soybean meal option. The discussion traces the discrepancy to a change in OptionMaster’s Greek conventions across versions. Earlier versions…

OptionsDerivatives pricingFuturesRisk management
BigQuant

This MATLAB note introduces Monte Carlo estimation through repeated random sampling. It uses the Monty Hall problem to illustrate estimating a win probability by switching doors, and compares simulated frequencies with the theoretical probability. It then…

StatisticsBacktestingEquitiesOptions
Amberdata research

This market recap describes how a sharp Bitcoin and Ether sell-off affected realized and implied volatility, term structure, option skew, trading flows, and dealer gamma. Short-dated put demand pushed weekly skew into put premium, while longer-dated skew…

CryptoOptionsVolatilityDerivatives pricing
FMZ forum

This tutorial explains dynamic delta hedging for cryptocurrency options, using Deribit options and perpetual futures as its example. It introduces delta, gamma, theta, and vega, then describes offsetting an option position’s changing delta with futures. For…

OptionsDerivatives pricingPerpetual futuresRisk management
Amberdata research

The document compares free and paid crypto options scanners for professional trading teams. It explains that scanners consolidate activity across exchanges and that richer tools can report net volume and positioning, delta-based butterfly measures, block and…

CryptoOptionsVolatilitySentiment
vn.py community

This short forum exchange explains why a minute-bar option strategy cannot simply schedule orders to run every few seconds. In the described framework, strategy callbacks are driven by incoming market data rather than by a clock. One reply says…

OptionsExecutionHigh-frequency trading
Amberdata research

This market commentary argues that implied volatility in crypto options appears low relative to an uncertain macroeconomic backdrop. It points to volatility measures and charts for Bitcoin, Ether, Solana, bonds, equities, and precious metals, describing a…

CryptoOptionsVolatilityDerivatives pricing