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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

9,797 documents

FMZ forum

The article outlines a long-only strategy for crypto contracts that attempts to buy during declines using a grid-like sequence of limit orders. Starting from a reference price, it places further buys at progressively lower intervals, with order size…

CryptoFuturesGrid tradingPosition sizing
MQL5 code base

The document explains a volume indicator that compares trading activity with each candle’s price result. It distinguishes ordinary activity from unusually high or low volume and labels patterns such as strong bullish or bearish participation, climax volume,…

Technical indicatorsMarket microstructureForexCrypto
vn.py community

The document explains why a futures product may appear with different letter casing in public exchange materials and trading software. Using Shanghai Futures Exchange rebar as its example, it distinguishes the public-facing symbol from the machine-readable…

FuturesChina marketsExecution
Kraken Learn

The document outlines how eligible futures traders can use digital assets such as Bitcoin or Ether as collateral for USD-margined contracts. This can let a trader retain exposure to those assets while using their value to support futures positions, including…

CryptoFuturesRisk managementDerivatives pricing
SuperMind

The example pairs a ten-day simple average of futures closing prices with the prior close to control a short position. It opens a short position when the prior close is below the average and the existing position is under a stated size threshold. When the…

FuturesTechnical indicatorsTrend followingExecution
Amberdata research

This market snapshot examines the surge in memecoin activity and its effects across centralized exchanges, futures, decentralized exchanges, lending markets, and blockchain networks. It connects demand for tokens such as PEPE and Bitcoin’s BRC-20 assets with…

CryptoMarket microstructureSentimentOn-chain data
TqSdk

This reference explains commonly used fields in market data objects and in futures and stock trading objects. For quotes, K-lines, and ticks, it identifies prices, timestamps, volume, open interest, and other session or contract details. Separate tables…

FuturesEquitiesExecutionMarket microstructure
FMZ forum

This essay groups futures-market participants into exchanges and brokers, commercial firms linked to the underlying supply chain, institutional or large traders, and ordinary individual traders. It argues that exchanges and brokers earn transaction fees,…

FuturesMarket microstructureExecutionRisk management
MQL5 code base

This document explains a charting script that identifies upper and lower DeMark pivot points and labels each point with a dimension. The dimension is determined by comparing a bar’s high or low with neighboring bars, expanding the comparison outward until…

Technical indicatorsFuturesBacktesting
Awesome Systematic Trading

This QuantConnect example implements a monthly momentum strategy across eight currency futures. It measures each contract’s return over roughly twelve months, ranks the available contracts, then targets the three strongest positions long and the three…

FuturesForexMomentumFactor investing
MQL5 code base

This document describes an educational MetaTrader 5 expert advisor that trades breakouts of a range formed from the highest high and lowest low over a configured number of completed bars. At a specified server time, it checks whether the range width falls…

FuturesBreakoutVolatilityPosition sizing
FMZ forum

The post describes a change in an automation workflow: indicator alerts containing simple command names previously triggered FMZ actions, but strategy-generated alerts now reach FMZ without opening trades. In the TradingView example, order entries attach…

ExecutionFutures
BigQuant

This forum entry shows a BigQuant initialization example for setting futures commissions through a per-contract commission object. The configuration maps an instrument to separate rates for opening, closing, and same-day closing trades, then passes the…

FuturesBacktestingRisk managementExecution
vn.py community

A trader reports that a futures strategy begins with a position value of negative one and does not act on a later exit signal. The problem appeared after two strategies traded the same contract. Orders were not visible in the strategy’s order callback, and…

FuturesExecution
FMZ forum

This article outlines a high-frequency strategy research framework for China’s order-driven markets, using Level 1 order book data and support vector machines (SVMs). It proposes extracting features such as best bid and ask prices and sizes, derived depth…

FuturesMachine learningHigh-frequency tradingMarket microstructure
NautilusTrader

This integration guide explains how a trading system connects to Deribit for market data and order execution. It outlines the division between WebSocket use for streaming and trading operations and HTTP use for instrument loading, historical requests, and…

CryptoFuturesOptionsExecution
Amberdata research

This market commentary connects macro events and crypto derivatives positioning with a bullish view on Bitcoin and possible catalysts for Ether. It discusses implied and realized volatility, options open interest and skew, futures basis, and perpetual…

CryptoOptionsFuturesPerpetual futures
TqSdk

This code builds a synthetic steel mill profit spread from daily futures prices for rebar, iron ore, and coke. It calculates the spread as rebar minus weighted quantities of the two inputs, smooths it with a 15-day moving average, and estimates a standard…

FuturesCommoditiesMean reversionPairs trading
TqSdk

This example builds a daily direction classifier for a rubber futures contract. Near a scheduled end-of-day cutoff, it uses recent closing prices to calculate three technical features, aligns each historical feature row with the following session's…

FuturesMachine learningTechnical indicatorsBacktesting
vn.py community

VeighNa Fusion is presented as a standardized futures trading platform distributed to customers through partner futures firms. It combines an execution environment, market data tools, CTA strategy development, historical backtesting, and an AI assistant. The…

FuturesBacktestingTechnical indicatorsRisk management
TqSdk

This code example implements an intraday R-Breaker strategy for a futures contract. It calculates seven reference levels from the previous daily bar: a pivot, breakout thresholds, setup levels, and reversal entry levels. When flat, price crossing a breakout…

FuturesBreakoutMean reversionTechnical indicators
vn.py community

This community post asks how to adapt VeighNa’s double moving-average example for daily bars in live trading and backtesting. The author describes sending ticks to a BarGenerator configured for daily aggregation, binding a daily callback, and loading…

FuturesTechnical indicatorsTrend following
Awesome Systematic Trading

This QuantConnect-style algorithm uses monthly observations of crude oil and E-mini S&P 500 futures to estimate whether equity exposure is attractive. It aligns the futures price histories, calculates their returns, then fits a simple linear regression with…

FuturesCommoditiesEquitiesStatistics
ProRealCode

The indicator marks the high and low during the first ten minutes of the DAX session and a corresponding ten-minute period around the US market open. It presents those range extremes as potential intraday support and resistance levels, with breaks beyond…

FuturesBreakoutTechnical indicators