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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

12,303 documents

MQL5 code base

This note describes a MACD variant that uses an RMA average in its calculation instead of the more familiar EMA. It characterizes the resulting values as comparatively fast and points to a potentially distinctive response after periods of high volatility.…

Technical indicatorsVolatility
Amberdata research

This market snapshot interprets crypto conditions across derivatives, spot flows, order books, stablecoins, and DeFi lending. It describes continued long liquidations in BTC, ETH, and SOL, funding turning negative across major assets, and BTC–ETH correlation…

CryptoPerpetual futuresMarket microstructureVolatility
SuperMind

This stock-selection rule screens Chinese equities using three conditions: daily amplitude above 1%, at least two limit-up events in the preceding 500 days, and a 2019 dividend ratio above 25%. The post describes these as signals of strong price action and…

EquitiesChina marketsVolatilityTechnical indicators
Amberdata research

This market note reviews Bitcoin and Ether options around a period of banking stress and major US economic releases. It describes Bitcoin’s term structure moving into backwardation, with near-dated implied volatility elevated relative to longer maturities,…

CryptoOptionsVolatilityDerivatives pricing
Amberdata research

This market commentary examines crypto options positioning in December 2023 as traders anticipated a US spot Bitcoin ETF decision. It discusses a sharp Bitcoin pullback and a rise in realized volatility, alongside a decline in short-dated implied volatility…

CryptoOptionsVolatilityEvent-driven
SuperMind

This SuperTrend variation adapts its ATR multiplier to the ratio of current ATR to a smoothed ATR average, widening bands in relatively volatile conditions and narrowing them when volatility is subdued. It adds an ATR-based cushion beyond the band before…

Trend followingVolatilityTechnical indicatorsMomentum
SuperMind

The article proposes screening stocks for a daily price range above 1%, no limit-up close on the previous day, and a positive gain over the preceding ten days below 35%. It presents the range as a way to find more active shares and treats the prior-day…

EquitiesChina marketsMomentumVolatility
ProRealCode

The document describes an indicator that compares recent volatility with a longer-term volatility baseline. It calculates standard deviations of close-to-close momentum over short and long lookback periods, then divides the short-period value by the…

VolatilityTechnical indicatorsStatistics
SuperMind

This document presents a Chinese A-share screening rule for robot-themed stocks. It combines daily amplitude above 1%, a KDJ value below 20, and floating market capitalization below 10 billion yuan. It also gives example implementations using a stock…

EquitiesChina marketsTechnical indicatorsVolatility
ProRealCode

This indicator uses a smoothed Z-score of closing prices to identify statistically stretched conditions, then measures the price range formed during the extreme. A move down through the oversold threshold arms a bullish box, while a move up through the…

Technical indicatorsBreakoutStatisticsVolatility
SuperMind

This screen selects Chinese equities with a daily high-low range above 1%, a low price between 4% and 5% below the previous close, and turnover between 3% and 12%. The author interprets the range as a sign of elevated volatility, the steep decline as a…

EquitiesVolatilityMean reversionTechnical indicators
SuperMind

This Chinese stock-screening post proposes combining amplitude above 1, a shortening negative MACD histogram on a 15-minute chart, and a volume ratio between 1.5 and 6. The rationale is to find volatile shares with active trading while a bearish MACD…

EquitiesChina marketsTechnical indicatorsMomentum
MQL5 code base

This brief post presents a modified Damiani Volatmeter indicator for MetaTrader 5. Its author says a subtle implementation error caused the indicator to produce different values in visual backtesting, non-visual backtesting, and live charts. The stated…

VolatilityTechnical indicatorsBacktesting
MQL5 code base

The document describes an adaptive channel indicator that combines two adaptive components: an ATR-based channel width and a central value calculated as an adaptive exponential moving average. Both adjustments use Kaufman’s efficiency ratio, which allows the…

Technical indicatorsVolatilityTrend following
Amberdata research

This weekly digital-asset snapshot combines price action with derivatives positioning, institutional flows, order-book liquidity, spreads, and DeFi indicators. It describes Bitcoin testing support near $86,000 amid a broader risk-off move, while ETF…

CryptoPerpetual futuresMarket microstructureVolatility
Amberdata research

This market commentary links changing Federal Reserve rate-cut expectations with risk-asset performance, then examines relative strength and options conditions in Bitcoin and Ether. It discusses how realized volatility and the ETH/BTC ratio may reflect…

CryptoOptionsVolatilityDerivatives pricing
SuperMind

This document describes a stock-selection rule for A-shares using three conditions: price amplitude above 1%, a 2021 date filter, and best bid volume greater than best ask volume. It presents the rule as a way to identify more volatile stocks with potential…

EquitiesVolatilityMarket microstructureTechnical indicators
Amberdata research

This market commentary interprets crypto option-implied distributions and volatility around the 2024 US election. It uses calibrated BTC options distributions to compare the market-implied chance of prices above $72,000 at the November 8 and December 27…

CryptoOptionsVolatilityDerivatives pricing
ProRealCode

The document presents an indicator intended to display a daily SuperTrend line on a lower-timeframe chart for intraday use. It derives a daily true-range measure from daily highs, lows, and prior closes, then forms upper and lower bands around the daily…

Technical indicatorsVolatilityTrend followingFutures
ProRealCode

The document outlines a way to weight stock positions using a momentum signal and an estimate of volatility. It describes two alternatives: an exponentially weighted moving average based on squared returns, and a Yang–Zhang variation of Garman–Klass…

EquitiesMomentumVolatilityPosition sizing
Cryptohopper blog

The article explains how Bollinger Bands can help traders assess crypto volatility and possible market direction. The bands are described as a moving average with upper and lower boundaries typically set two standard deviations away. Narrowing bands indicate…

CryptoTechnical indicatorsVolatilityBreakout
SuperMind

This proposed equity screen selects stocks with amplitude above 1, an RSI below 65, and prior-day turnover above 60 million. It combines a price-range measure with a momentum oscillator and a liquidity threshold, aiming to find stocks with activity while…

EquitiesTechnical indicatorsMomentumVolatility
SuperMind

This stock-selection screen combines price amplitude above 1, a dividend-related payout ratio above 25% for 2019, and a rising-bottom condition. The explanation treats larger amplitude as a sign of greater trading opportunity, the dividend filter as a way to…

EquitiesTechnical indicatorsVolatilityRisk management