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

1,124 份文件

Machine Learning for Trading

This tutorial constructs a continuous futures series from individual ES contracts. It identifies the front month using trading volume, with a monotonic roll constraint, and compares volume-based switching with calendar-based timing. It explains why outright…

期貨回測統計交易執行
Machine Learning for Trading

This notebook turns a real supply-chain knowledge graph and institutional 13F holdings into machine-learning features. It derives network measures such as centrality and dependency indicators, estimates supplier concentration and competitive exposure, and…

機器學習股票投資組合建構統計
Machine Learning for Trading

This notebook examines AAPL trade and quote records during the March 16, 2020 market crash to show how market microstructure changes under stress. It filters the tape to regular trading hours, distinguishes trade prints from national best bid and offer…

股票市場微結構交易執行波動率
Machine Learning for Trading

This tutorial builds a monthly ETF rotation simulator using a trailing risk-adjusted momentum ranking and a Treasury yield-curve regime filter. At each month-end, the rule ranks ten funds using their recent price history; risk-on months hold the top three…

回測動能投資組合建構交易執行
Machine Learning for Trading

This notebook demonstrates an operational cycle for an ETF prediction strategy: refresh market data, recompute financial features, retrain a Ridge model, save deployment artifacts, generate current cross-sectional predictions, replay the predictions through…

股票機器學習交易執行回測
Machine Learning for Trading

This notebook compares LightGBM with a minimal multilayer perceptron, TabM, and optionally TabPFN for cross-sectional ETF return prediction. Models are evaluated on shared walk-forward folds using rank information coefficient, with chronological validation…

機器學習股票統計回測
Machine Learning for Trading

This notebook develops a framework for translating market activity into modeled trading costs and strategy capacity. It introduces the square-root impact model, identifies volatility and average daily volume as key inputs, and distinguishes quantities…

市場微結構交易執行風險管理部位規模
Machine Learning for Trading

This document compares four equity market data sources for studying trades, quotes, and limit order books: AlgoSeek TAQ, Databento market by order, NASDAQ ITCH, and IEX HIST. It explains each source’s granularity, coverage, cost or access conditions, storage…

股票市場微結構交易執行高頻交易
Machine Learning for Trading

This notebook isolates results for the LEAN engine from an audit comparing retained real-strategy runs with matching ML4T Backtest profiles. It identifies four supported workloads: ETF allocation, crypto perpetual funding, USD-quoted FX allocation, and a US…

多資產回測交易執行加密貨幣
Machine Learning for Trading

This notebook introduces a workflow that connects standardized ETF data loading, a feature registry, and signal diagnostics. It shows how to discover indicator metadata, compute features using defaults or explicit parameters, and store configurations for…

股票技術指標動能統計
Machine Learning for Trading

This notebook compares a Random Forest with XGBoost, LightGBM, and CatBoost on a panel of anonymized firm characteristics and returns. The dataset supplies temporal training, validation, and test splits. Models are evaluated using Spearman rank information…

股票機器學習統計因子投資
Machine Learning for Trading

The notebook explains value at risk as a loss quantile and conditional value at risk, or expected shortfall, as the average loss beyond that threshold. It estimates one-day tail risk for a broad equity ETF using four approaches: empirical historical…

股票波動率風險管理統計
Machine Learning for Trading

This notebook describes how to produce holdout predictions for a selected crypto perpetuals funding strategy. It resolves the configuration from validation results, carries its checkpoint choice forward, and builds a new training specification using data…

加密貨幣永續期貨機器學習回測
Machine Learning for Trading

This notebook assesses a selected S&P 500 equity and options strategy by reconstructing its validation configuration from registered, full-coverage candidates. It traces the funnel from equal-weight baseline through allocation, risk controls, and cost…

股票選擇權回測風險管理
Machine Learning for Trading

This notebook explains Hierarchical Risk Parity as an alternative to mean-variance allocation when covariance estimates are noisy. HRP clusters assets using correlation distances, orders them according to the hierarchy, then recursively bisects the ordered…

投資組合建構風險管理回測股票
Machine Learning for Trading

This notebook presents a cross-case-study inventory of engineered financial features and the strongest registry information coefficient for each market study. It counts features and feature-name families, compares feature-space sizes, and summarizes…

多資產統計機器學習投資組合建構
Machine Learning for Trading

The document explains how to identify market regimes by dividing returns into overlapping windows and treating each window as an empirical distribution. It measures the distance between windows through matched, sorted quantiles, so differences across the…

統計機器學習風險管理回測
Machine Learning for Trading

This notebook assesses a weekly S&P 500 options straddle strategy using registered backtests and a fixed selection process. It resolves the selected configuration from the registry after applying a liquid-universe restriction, then evaluates validation and…

選擇權股票回測風險管理
Machine Learning for Trading

This study builds a minute level measure of order flow pressure from market by order data for NVDA. It signs order additions, cancellations, and fills by side, weights events by size and distance from the midpoint, and smooths the resulting pressure series.…

股票市場微結構交易執行統計
Machine Learning for Trading

This notebook presents a conditional autoencoder that preserves the structure of an instrumented latent factor model while replacing the linear map from firm characteristics to factor exposures with a neural network. Monthly returns are represented as…

股票機器學習因子投資統計
Machine Learning for Trading

This document explains how to create predictions for a reserved NASDAQ-100 microstructure holdout period using a configuration selected on validation data. It refits the chosen model using only history that ends before the holdout, with a label buffer wide…

回測風險管理市場微結構
Machine Learning for Trading

This case study fits regularized linear models to returns from short at-the-money straddles held to expiry. Because premium income caps the gain while losses can grow without bound, a few severe losses dominate a squared-error regression objective. The…

選擇權波動率機器學習風險管理
Machine Learning for Trading

This notebook uses real ETF data to assess whether a momentum signal remains useful under reasonable changes to its lookback, market regime, and implementation. It computes cross-sectional information coefficients between momentum and forward returns, then…

期貨動能回測統計
Machine Learning for Trading

This notebook compares two locally run, open-weight embedding models on a retrieval task using passages from recent 10-K filings for a small set of large US companies. Both models receive the same two-sentence passages and a fixed set of queries spanning…

機器學習統計股票美國市場