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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

ProRealCode

The document describes a long-only strategy for the DAX using four-hour bars. It combines a detrended price oscillator with a Repulse indicator intended to capture candle buying and selling pressure. The system opens a long position when both readings exceed…

FuturesTrend followingTechnical indicatorsRisk management
TqSdk

This reference explains common market-data workflows in the TqSdk Python interface. It covers subscribing to real-time quotes, monitoring updates to quote fields, and requesting K-line bars or tick series as data frames that update in place. It also…

FuturesMarket microstructureExecution
vn.py community

A user reports that minute-bar open and close prices generated from CTP market data sometimes differ from values shown in mainstream trading software, with discrepancies of about one currency unit. The reply identifies a timestamp convention as a possible…

FuturesMarket microstructureStatistics
BigQuant

This study compares ways to hedge stock holdings, motivated by investors who hold equities as a base for IPO subscriptions and want to reduce exposure to price swings. It measures hedge costs using annualized futures discounts and compares those costs across…

FuturesOptionsEquitiesRisk management
BigQuant

This article explains how to assess a systematic futures model through test design and several complementary performance measures. It recommends testing across multiple instruments and long historical periods, and including commissions and slippage. The…

FuturesBacktestingRisk managementExecution
TqSdk

This tutorial explains the true range (TR) and average true range (ATR), then shows how to calculate and display both for a Shanghai Futures Exchange gold contract using TqSdk. True range takes the largest of the current high-low range, the gap between the…

FuturesTechnical indicatorsVolatility
FMZ forum

This document is a dated overview of trading-fee arrangements at cryptocurrency exchanges, grouped into domestic and international sections. It distinguishes fixed-rate charges from maker-taker schedules and includes spot and futures fee references. For some…

CryptoExecutionMarket microstructureFutures
FMZ forum

This article discusses practical decisions for running systematic strategies live, including how to enter a system initially, define the active futures contract during roll periods, choose markets, manage capital, handle outages or unexpected events, prepare…

FuturesRisk managementPosition sizingExecution
BigQuant

This research summary describes a machine-learning approach to predicting very short-term price changes from futures order-book data. It proposes using support vector machines with features such as best bid and ask prices and sizes, order-book depth, slope…

FuturesMachine learningHigh-frequency tradingMarket microstructure
BigQuant

This article presents the volume clock as an event-based way to measure market activity, in contrast with fixed calendar time. Sampling equal-volume intervals can reduce intraday seasonal effects and may produce observations better suited to conventional…

FuturesHigh-frequency tradingMarket microstructureExecution
OctoBot

This release note describes new features and fixes in OctoBot 1.0.4. Users can download strategies from OctoBot Cloud into a self-hosted bot, then configure them for different exchanges or assets and use the platform’s backtesting or strategy design tools to…

CryptoBacktestingFuturesExecution
vn.py community

This document lays out a sequence of practical exercises for learning quantitative trading with VN.PY. The projects cover market data retrieval and database storage, vectorized indicator calculations, CTA strategy development, cleaning futures data, and…

FuturesBacktestingStatisticsRisk management
BigQuant

This platform update describes additions to an expression engine for quantitative analysis. The new rolling functions calculate sums, means, standard deviations, variances, skewness, and kurtosis over a specified lookback while handling missing values. It…

StatisticsFuturesMomentumTechnical indicators
vn.py community

This community post reports a failure in a VeighNa CTA backtest on Windows with a Tushare data service. Historical one-minute futures data downloads successfully and the strategy loads, but the run fails when the backtesting engine calculates performance…

BacktestingFuturesStatistics
BigQuant

The study examines timing the basis between Chinese equity indices and their futures, motivated by persistent futures discounts and uncertainty for market-neutral funds. To reduce the influence of carry components such as rates, dividends, and time to…

FuturesEquitiesMean reversionMomentum
ProRealCode

The DELOREAN sto SC-SV v.2.0 is described as a stochastic-style oscillator intended for volatile instruments, with Bitcoin and DAX40 given as examples. It first locates the close within a rolling high-low range, smooths that reading into K and D lines, then…

Technical indicatorsVolatilityCryptoFutures
Amberdata research

This market commentary links the inflation outlook and Federal Reserve communications to crypto sentiment, then focuses on Bitcoin positioning ahead of the April 2024 halving. It notes the reported PCE and core PCE readings, upcoming employment data, strong…

CryptoOptionsFuturesVolatility
BigQuant

This research note introduces time-series stationarity as preparation for studying pairs trading in cryptocurrency futures. It explains weak stationarity through stable mean and variance and covariance that depends on the time gap rather than the observation…

CryptoFuturesPairs tradingStatistics
ProRealCode

The document describes a multi-day strategy for DAX contracts for difference using hourly signals. A QQE indicator supplies long and short triggers, while a Universal indicator acts as a directional filter. Separate month-specific multipliers scale long and…

FuturesTechnical indicatorsTrend followingBacktesting
FMZ forum

The author explains why Bitcoin markets can suit systematic trading, citing continuous access, low entry barriers, active spot and futures markets, and opportunities across platforms. The main lesson is to understand the economic reason a strategy should…

CryptoArbitrageFuturesSpot markets
MQL5 code base

This MetaTrader 5 expert advisor combines Supertrend direction with ADX trend strength and the relative positions of +DI and −DI. It checks signals once per new signal-timeframe bar using the prior completed candle. A long or short entry requires the…

FuturesTrend followingTechnical indicatorsRisk management
vn.py community

A VeighNa community thread addresses why the official spread backtesting example cannot read data even though the same data source works in CTA backtesting. The response explains that spread-trading backtests require their own prepared dataset: data must…

BacktestingPairs tradingFutures
MQL5 code base

The document explains tick volume as a proxy for intraday trading activity, especially in futures markets where reported contract volume may arrive with a delay. It counts the number of deals or ticks within a chosen interval, making it available sooner than…

FuturesMarket microstructureTechnical indicators
MQL5 code base

This indicator plots account balance and equity over trading history, with optional displays for used margin and free equity. It can also show period returns, maximum drawdown in absolute and percentage terms, profit factor, and recovery factor. Filters…

Risk managementStatisticsFuturesForex