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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
Quantpedia
86 documents
TqSdk
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
Quantopian lectures
45 documents
Binance API docs
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

79,386 documents

ProRealCode

The document describes an attempt to compress several Ichimoku comparisons into a separate histogram so that signals can be viewed without adding clutter to the main price chart. Five conditions compare the conversion and base lines, current and lagged…

Technical indicatorsTrend followingEquities
SuperMind

This stock-selection proposal combines three filters: membership in the metaverse theme, positive net buying attributed to major participants during the opening auction, and a close above the middle Bollinger Band but below its upper band. The article…

EquitiesChina marketsTechnical indicatorsSentiment
BigQuant

The article presents five principles for short-term stock trading: prominent stocks may attract liquidity despite looking expensive; near-term prices reflect the balance of buying and selling shaped by expectations and sentiment; traders should seek gaps…

EquitiesSentimentMomentumMarket microstructure
BigQuant

The study measures a fund’s risk shifting by comparing the volatility implied by its latest disclosed holdings with the fund’s realized volatility over the same rolling period. Using quarterly holdings and return data for actively managed US domestic equity…

EquitiesRisk managementFactor investing
NautilusTrader

The document explains configuration conventions in NautilusTrader, covering typed settings for data and execution clients, engines, and strategies. It distinguishes concrete fields from optional fields, whose absent values can mean disabled behavior, an…

ExecutionRisk management
SuperMind

This document presents a China A-share screen centered on turnover between 3% and 12%, a seven-day declining-price condition, current volume above 10,000 lots, and an opening price above the previous close. Its final proposed version adds market…

China marketsEquitiesMean reversionTechnical indicators
ProRealCode

This indicator constructs Heikin-Ashi values from open, high, low, and close prices, then smooths each component using a configurable average type and period. It draws candles using the smoothed open and close, while the channel boundaries come from the…

Technical indicatorsTrend followingVolatility
vn.py community

This brief forum exchange answers whether VeighNa, also known as vn.py, requires Tushare as the sole source of historical A-share data for backtesting. The response says the framework supports multiple data services and points readers to its documentation…

China marketsEquitiesBacktesting
SuperMind

This document describes a stock screen combining turnover, parent-company net profit growth, and a moving-average trend filter. It selects shares with turnover between 3% and 12%, year-over-year net profit growth above 20% and at most 100%, and a 20-day…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This Chinese-language post proposes screening stocks using three conditions: a large daily price range, prior-day turnover within a specified band, and a reversal candle pattern. It presents the combination as a way to find stocks with substantial price…

EquitiesTechnical indicatorsMean reversionChina markets
MQL5 code base

This indicator description explains settings for changing the geometry of Fibonacci levels displayed by a candle-based automatic Fibonacci tool. A width multiplier scales the distance of the levels from the zero level, while leaving that zero level in place.…

Technical indicatorsFutures
BigQuant

This brief Chinese-language support note addresses how to use factors produced by a genetic factor-mining process. It says the discovered factor has an expression, but that a user must convert the expression manually before sending it to a factor analysis…

Factor investingMachine learning
MQL5 code base

VR Breakdown Level is a breakout strategy that records the high and low of a prior period, with the period length chosen in the trading robot’s settings. At the beginning of each new period, it saves those levels. If price crosses the prior high, it opens a…

BreakoutExecutionPosition sizingRisk management
MQL5 code base

DayMomentum is a momentum indicator whose calculation period adjusts automatically according to the number of bars in the current day. It is described as suitable for chart intervals ranging from one minute to one day, with the intended use being intraday…

MomentumTechnical indicators
SuperMind

The document proposes selecting stocks with amplitude above 1, a share price of 18.5 yuan, and company size above 200 million. It frames the screen as a way to find active stocks with meaningful scale, then acknowledges that size alone cannot assess…

EquitiesVolatilityTechnical indicatorsRisk management
SuperMind

The article proposes a short-term equity screen based on amplitude above 1, three consecutive prior daily gains that are not limit-up moves, and large-order net inflow during the afternoon. It interprets amplitude as a sign of an active security and…

EquitiesMomentumVolatilityMarket microstructure
SuperMind

The document describes an equity screen requiring price amplitude above 1, return on equity above 15% for five consecutive years, and more than three years since listing. It presents these conditions as a way to combine active trading with a record of…

EquitiesFactor investingVolatilityRisk management
SuperMind

The article presents a notebook-based workflow for quantitative research: obtain exchange candlestick history through an API, store and inspect it with pandas, plot price and trade-flow measures, and build a Python backtest for multiple spot or perpetual…

CryptoPerpetual futuresBacktestingStatistics
MQL5 code base

This brief entry identifies an indicator that applies Joe DiNapoli’s approach to the ZigZag, a charting tool used to mark significant price swings while filtering smaller movements. It attributes the implementation to an author named CrazyChart and notes…

Technical indicators
SuperMind

The article describes a short-term A-share screening rule combining three conditions: prior-session price amplitude above 1%, circulating shares no greater than 5.5 billion, and a stock code beginning with 60. Its sample implementation intersects these…

EquitiesChina marketsVolatilityTechnical indicators
Amberdata research

This research summary examines Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect, comparing northbound and southbound trading and describing the traits associated with northbound holdings. It reports that flows did not reliably anticipate market…

China marketsEquitiesFactor investingMarket microstructure
Stratmill research code

This module constructs a continuous futures series by identifying contract roll dates and calculating the price gap between the expiring contract and the next contract. It accumulates those gaps through time and can align the adjusted series at its end. A…

FuturesBacktestingCommoditiesStatistics