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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

32 documents

vn.py

This documentation explains how VeighNa Elite Trader’s option strategy module supports strategy setup, initialization, automated trading, monitoring, and removal. It describes the ContractManager’s role in loading daily contract information and maintaining…

OptionsDerivatives pricingExecutionBacktesting
vn.py

This guide explains the CTA strategy workflow in VeighNa Fusion, from connecting to the trading gateway and creating a strategy instance to configuring parameters, initializing, starting, and stopping it. Each instance has its own target contract, parameter…

FuturesExecutionRisk managementBacktesting
vn.py

This guide explains a configurable pre-trade risk engine that checks orders before they are sent through a trading API. Rules can be enabled in a JSON settings file and include symbol blacklists and whitelists, order size and value caps, cancellation limits,…

Risk managementExecutionPosition sizing
vn.py

The document explains how to use a market-depth trading interface for live, manual intraday trading in a single futures contract. After connecting a trading gateway and opening a contract chart, the ladder displays price levels, bid and ask quantities, best…

FuturesExecutionMarket microstructure
vn.py

This guide explains how to enable a trading gateway in VeighNa Station or load one from a startup script, connect through VeighNa Trader, and view account, position, order, trade, and contract information. Gateway settings can be edited in the application or…

FuturesOptionsEquitiesExecution
vn.py

This guide explains how to construct, monitor, and trade synthetic spreads in the VeighNa SpreadTrading module. A spread can combine several contract legs using a formula, including pricing legs that are not traded, which supports relationships involving…

FuturesCommoditiesArbitragePairs trading
vn.py

This operational guide explains how to run multi-contract portfolio strategies through VeighNa Elite Trader’s PortfolioStrategy module. It covers loading strategy classes, creating instances with symbols, gateways, and typed parameters, then initializing…

Multi-assetExecutionRisk management
vn.py

This guide explains how pre-trade controls can block orders that exceed preset limits, helping reduce accidental oversizing, excessive order flow, and other operational errors. It describes common controls for order frequency, reset intervals, single-order…

Risk managementExecutionPosition sizingBacktesting
vn.py

This guide describes an execution module that runs algorithms in a separate process, lets users configure and monitor orders, and supports manual order routing across multiple accounts. Its five examples illustrate different execution behaviors: TWAP divides…

ExecutionMarket microstructureFuturesHigh-frequency trading
vn.py

This operational guide explains how to route selected VeighNa Elite Trader logs to a DingTalk group through a custom chat robot. The setup requires creating the robot, enabling signed requests, and entering its token and signing secret in the platform’s…

ExecutionRisk management
vn.py

This guide explains how VeighNa Elite Trader’s CTA template can filter synthetic bars received outside configured trading sessions, preventing out-of-session data from affecting strategy indicators. It describes obtaining a sample filter configuration…

FuturesMarket microstructureExecution
vn.py

The document explains how to use VeighNa’s DataRecorder module to save live market data to a database. Recorded ticks and one-minute bars can later be viewed in DataManager, used for historical backtests, or supplied to trading strategies during live…

ExecutionBacktesting
vn.py

The document explains how VeighNa WebTrader provides browser access to basic manual trading functions. Its architecture separates the strategy trading process from a FastAPI web service. REST requests from the browser are relayed to the trading process…

ExecutionMarket microstructure
vn.py

This notebook demonstrates a basic command-line workflow for operating a trading engine through a CTP gateway. It loads connection settings, initializes the engine, and connects to the server. The example then queries available contracts, account balances,…

FuturesExecution
vn.py

The document explains how to connect VeighNa trading software to Excel through its ExcelRtd module and PyXLL. After installing and configuring the commercial PyXLL add-in, users can enable the module in VeighNa Station or load it in a startup script. The…

FuturesExecutionMarket microstructure
vn.py

These release notes describe changes across versions of the VeighNa trading framework. For quantitative research, notable updates include a cross-sectional percentile ranking function, revised factor and signal performance analysis, an added VWAP matching…

Machine learningFactor investingBacktestingExecution
vn.py

This example demonstrates a portfolio-strategy backtest for a pair trading strategy on two Dalian Commodity Exchange continuous contracts. It configures minute data over a specified historical interval and supplies commission rates, slippage, contract sizes,…

FuturesPairs tradingBacktestingExecution
vn.py

The document describes a software workflow for managing option volatility strategies. It covers model selection for European and American options, choosing a futures or synthetic underlying, monitoring option quotes and Greeks, and calibrating pricing…

OptionsVolatilityDerivatives pricingRisk management
vn.py

This VeighNa guide compares database choices for storing trading data, including embedded SQLite, relational systems such as MySQL and PostgreSQL, and non-SQL options such as MongoDB, InfluxDB, DolphinDB, Arctic, and LevelDB. It describes their broad storage…

Multi-assetMarket microstructureExecutionBacktesting
vn.py

This Python example describes a process manager for a CTA futures strategy using vn.py and the CTP gateway. A parent process checks the clock and launches a child process during configured daytime and overnight trading windows. The child creates the event…

FuturesExecutionChina markets
vn.py

This document explains a graphical workflow for researching CTA strategies with historical data. It covers downloading market data, configuring a backtest with instrument details and trading costs, and reviewing equity, drawdown, daily profit and loss, and…

BacktestingFuturesStatisticsRisk management
vn.py

This guide explains how to use VeighNa’s CTA strategy module to load strategy classes, create instances, initialize them with historical data, and start or stop automated trading. It describes how instances can trade different futures contracts with separate…

FuturesExecutionRisk managementBacktesting
vn.py

This documentation explains how to load and operate VeighNa’s CTA strategy module. It covers adding strategy instances, selecting contracts and parameters, loading historical data, restoring saved variables, subscribing to market data, and enabling automated…

FuturesExecutionRisk management