跳至內容

知識圖書館

這裡收錄 Stratmill 研究代理對 AI 代理閱讀過的書籍、論文、文章與程式碼所寫的摘要與核心觀點。每個頁面都連結至原始資料。

Quant Q&A
20,364 份文件
SuperMind
12,226 份文件
OKX Learn
8,431 份文件
Strategy library
7,910 份文件
MQL5 code base
7,090 份文件
BigQuant
3,481 份文件
Bitget Academy
3,298 份文件
MQL5 articles
3,012 份文件
TradingView scripts
1,976 份文件
ProRealCode
1,507 份文件
Deribit Insights
1,232 份文件
Machine Learning for Trading
1,124 份文件
arXiv papers
1,033 份文件
Amberdata research
766 份文件
FMZ forum
682 份文件
FMZ digest
662 份文件
vn.py community
560 份文件
QuantInsti blog
511 份文件
Galaxy Research
340 份文件
QuantStart
246 份文件
Stratmill research code
219 份文件
Robot Wealth
195 份文件
NautilusTrader
191 份文件
Hummingbot docs
181 份文件
Paradigm research
175 份文件
Lumibot
164 份文件
Kraken Learn
163 份文件
量化課程圖書館
157 份文件
OctoBot
152 份文件
Cryptohopper blog
144 份文件
Systematic trading blog (Rob Carver)
132 份文件
Qlib
116 份文件
TqSdk
86 份文件
Quantpedia
86 份文件
Hyperliquid docs
79 份文件
Freqtrade
68 份文件
Hudson & Thames
62 份文件
Awesome Systematic Trading
61 份文件
backtrader
54 份文件
vn.py
50 份文件
Binance API docs
45 份文件
Quantopian 講座
45 份文件
FMZ guides
38 份文件
pysystemtrade
34 份文件
Freqtrade docs
32 份文件
quant-trading
31 份文件
FinRL
28 份文件
Zipline
22 份文件
FMZ live strategies
21 份文件
Jesse
17 份文件
pyfolio
16 份文件
Alphalens
14 份文件
WonderTrader
14 份文件
backtesting.py
11 份文件
Technical Analysis
9 份文件
QTPyLib
8 份文件
QuantRocket
7 份文件
Lumibot strategies
7 份文件
Awesome Quant
1 份文件

搜尋圖書館

50 份文件

vn.py

The document introduces Zhice, a strategy research workspace within VeighNa Fusion. It describes a staged workflow that takes a user's trading idea through clarification of the logic, code drafting and review, backtesting, parameter optimization, and results…

機器學習回測風險管理
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,…

期貨配對交易回測交易執行
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…

選擇權波動率衍生品定價風險管理
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…

多資產市場微結構交易執行回測
vn.py

This data-preparation example builds a historical dataset for research on the CSI 300 and its constituent stocks. It retrieves the index membership history over a selected date range, converts provider-specific exchange symbols to the format used by the…

中國市場股票回測
vn.py

This document describes a guided process for turning a trading idea into a strategy that can be reviewed and tested. It moves through defining the idea, drafting and confirming its logic, generating and checking code, running a backtest, planning parameter…

回測風險管理部位規模
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…

期貨交易執行中國市場
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…

回測期貨統計風險管理
vn.py

This notebook outlines a daily equity research workflow using CSI 300 constituents. It loads historical bars, builds an Alpha158 feature dataset, and defines training, validation, and test periods. The target is a forward VWAP return over a three-day…

股票機器學習回測中國市場
vn.py

This document outlines a workflow for assembling historical data for a CSI 300 research project. It downloads historical constituent information, retrieves the index membership for each trading date, converts vendor symbols into vn.py format, and saves the…

股票中國市場回測
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…

期貨交易執行風險管理回測
vn.py

This guidance explains why AI-generated strategy logic, code, reviews, backtests, and parameter recommendations should be treated as research aids rather than final trading decisions. Outputs may contain errors or omissions, vary across models or settings,…

機器學習回測風險管理
vn.py

This example runs two existing futures strategies independently, using separate instruments, date ranges, trading costs, contract sizes, and capital settings. It then adds their result data frames, removes missing rows, and passes the combined data to a…

期貨回測投資組合建構
vn.py

This workflow demonstrates an equity prediction pipeline using CSI 300 constituent data, Alpha158 features, and a multilayer perceptron. It defines training, validation, and test periods, prepares constituent-filtered data, normalizes features using robust…

中國市場股票機器學習回測
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…

期貨交易執行風險管理
vn.py

This reference catalogs calculation functions available in the VeighNa Elite Trader CTA module. It groups common tools by their required inputs and outputs, covering moving averages, momentum and rate-of-change measures, volatility, trend strength and…

技術指標期貨交易執行
vn.py

The document explains how historical market data supports CTA strategy initialization, backtests, parameter optimization, and research. It outlines importing externally sourced data into a local environment and recommends checking file reliability, timestamp…

回測期貨風險管理
vn.py

This guide describes a user interface for running execution algorithms and explains how to configure an order’s instrument, side, price, quantity, duration, interval, and open-or-close instruction. It focuses on order execution rather than deciding what to…

交易執行市場微結構期貨風險管理
vn.py

This operational guide explains how to connect a VeighNa Fusion account to a CTP futures interface and check that contract lookup, market-data subscription, and order functions are working. It outlines prerequisites such as obtaining the correct broker…

期貨交易執行市場微結構
vn.py

The document explains how VeighNa’s PaperAccount module simulates trading against live market data while keeping orders local. It supports limit, market, and stop orders, with configurable slippage for market and stop executions. Orders generally wait for…

交易執行市場微結構風險管理回測
vn.py

This workflow demonstrates an end-to-end daily equity modeling process using CSI 300 constituents and vn.py’s AlphaLab tools. It loads constituent histories, builds an Alpha158 dataset, and divides observations into training, validation, and test periods.…

股票機器學習統計回測
vn.py

The document explains a plugin-based pre-trade risk module for VeighNa trading systems. Its built-in rules can cap active orders and daily order, cancel, and trade counts; detect repeated identical orders; limit order size or notional value; and validate…

風險管理交易執行期貨中國市場
vn.py

The document explains how VeighNa’s RPC service lets one trading process act as a server for separate client processes. Using ZeroMQ, the server accepts requests such as market-data subscriptions, orders, cancellations, and account queries, while…

交易執行市場微結構風險管理
vn.py

This notebook outlines an end-to-end equity alpha research workflow for CSI 300 constituents. It loads daily constituent data, constructs an Alpha158 dataset, divides observations into training, validation, and test periods, applies missing-label removal and…

股票中國市場機器學習回測