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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

SuperMind

This draft presents a research framework for estimating short-horizon crypto prices from fixed-interval data. It resamples futures and spot mid-prices at 100-millisecond intervals, derives returns, and preprocesses order-book imbalance features. The proposed…

CryptoFuturesSpot marketsMarket microstructure
BigQuant

This project outlines a mean-reversion pairs strategy for Chinese futures. It screens candidate contract pairs for cointegration with an Augmented Dickey-Fuller test, estimates a changing hedge ratio with a Kalman filter, and measures spread behavior with…

FuturesChina marketsPairs tradingMean reversion
NautilusTrader

This tutorial demonstrates replaying Binance level-two order book snapshots and updates in a backtest engine. It describes rebuilding the book from timestamped deltas, then checking the best bid and ask sizes after each update. When the larger side exceeds a…

CryptoFuturesBacktestingMarket microstructure
TqSdk

The document explains how to retrieve futures margin rates through TqSdk2 when a trading program otherwise uses TqSdk. It describes running both libraries in one Python file: connect directly to a CTP broker through TqSdk2, query the margin rate for a…

FuturesRisk managementExecution
ProRealCode

This post presents a 15-minute intraday strategy for German DAX mini CFDs. It uses a Super Smoother filter applied to price changes, then normalizes the filtered value by a decaying estimate of its peak magnitude. The strategy opens long positions when the…

FuturesTechnical indicatorsTrend followingRisk management
BigQuant

This excerpt summarizes a Chinese weekly research note on machine learning and commodity trading advisor strategies. It reports recent results for a neural network strategy on the CSI 500, a commodity futures strategy, and a commodity strategy combining…

Machine learningFuturesEquitiesCommodities
MQL5 code base

This educational MetaTrader 5 example extends the Commodity Channel Index by adding a user-defined shift in bars to its plotted values. The description explains the general implementation idea: apply the shift by changing the indicator buffer index, a…

Technical indicatorsFutures
MQL5 code base

This automated strategy places buy stop and sell stop orders around the high and low of the previous five-minute candle, with an adjustable offset. It can place additional pending orders relative to open positions. The cycle closes all positions when…

FuturesBreakoutPosition sizingBacktesting
MQL5 code base

This MT5 monitoring indicator summarizes account-level risk without placing or managing trades. It compares balance with equity to display current drawdown as both a percentage and a currency amount, and tracks peak equity to measure equity drawdown over…

Risk managementPosition sizingFutures
ProRealCode

This strategy description proposes trading intraday reversals around levels derived from daily bars. It buys when price is below a recent daily low and sells short when price moves above a recent daily high, aiming to capture a return toward the prior day’s…

FuturesMean reversionExecutionRisk management
pysystemtrade

This short Python example shows how to assemble a daily futures trading system with an order simulator. It creates a data source, loads configuration, and constructs a system from account, portfolio, position-sizing, forecast-combination, forecast-scaling,…

FuturesBacktestingExecution
vn.py community

This forum exchange explains why a futures backtest can differ from a course example even when the strategy and settings are the same: the data series may be revised over time. It describes the platform’s 888 series as a continuously smoothed main-contract…

FuturesBacktestingStatistics
FMZ forum

The document surveys systematic fund approaches and the markets where they are commonly used. It distinguishes trend following, countertrend trading, statistical arbitrage, convertible arbitrage, fixed income trades, commodity spreads, and global macro or…

Multi-assetTrend followingMean reversionArbitrage
ProRealCode

This strategy trades five-minute Dow index bars when a bullish or bearish engulfing candle forms with a body exceeding a multiple of average true range. Two Bollinger-style bands, calculated from moving averages and standard deviations, provide trend…

FuturesTechnical indicatorsVolatilityRisk management
vn.py community

This forum exchange explains that a VeighNa CTA strategy’s operating interval depends on its implementation. A strategy can react to each incoming tick, process one-minute bars formed from ticks, or aggregate those bars into longer periods using a bar…

FuturesTechnical indicatorsRisk managementExecution
TqSdk

This guide explains how to use TargetPosTask to move a contract’s net position toward a requested target. Create one task per contract, set a positive, negative, or zero target for long, short, or flat exposure, and keep calling the update loop so the task…

FuturesExecutionPosition sizingMarket microstructure
Amberdata research

This weekly crypto market snapshot combines regulatory and company news with indicators of institutional activity and derivatives positioning. It covers the launch of XRP and Dogecoin ETFs, Tether’s U.S.-focused stablecoin, a large corporate Solana purchase,…

CryptoFuturesSentimentMarket microstructure
QTPyLib

The document explains three QTPyLib utilities for working with Interactive Brokers futures. A tuple-generation helper builds a valid contract specification from a symbol, expiry, and optional exchange. Another helper selects the most active contract using…

FuturesPosition sizingRisk management
ProRealCode

The author describes an automated version of a manual DAX day-trading approach on five-minute charts. It uses 20-, 180-, and 200-period moving averages to define trend alignment and slope. Long entries require the averages to be ordered upward, all three to…

FuturesTrend followingTechnical indicatorsBacktesting
vn.py community

A Chinese forum exchange discusses large differences between futures data from Xuntouyan and TQSDK and the resulting disagreement in strategy backtests. The original poster reports discovering that the unusually high returns from one Xuntouyan test came from…

FuturesBacktesting
Stratmill research code

The Rust module outlines a connector for Binance USD-M futures that combines market data subscriptions, user account updates, and order management. It reads connection and credential settings from configuration, tracks registered symbols, and starts…

FuturesExecutionMarket microstructure
vn.py community

This forum exchange clarifies how VeighNa’s spread trading callbacks relate. For live operation, the strategy receives a complete spread data structure through `on_spread_data`; that structure can be converted into a tick before the tick-handling logic is…

FuturesPairs tradingBacktesting
vn.py community

The discussion explains that VeighNa 2.7 does not provide built-in save-and-restore support for variables in spread trading strategies, even though CTA strategies have a dedicated data file. As a workaround, a community reply suggests saving selected values…

FuturesExecution
NautilusTrader

This technical guide explains how NautilusTrader’s Interactive Brokers adapter connects to Trader Workstation or IB Gateway for market data, order execution, instrument discovery, and historical requests. It covers socket access, paper and live connection…

ExecutionMarket microstructureEquitiesFutures